From c1678a5b4af4275f9fedc90a4dff3a23db12674a Mon Sep 17 00:00:00 2001 From: RankKCodeTalker <93417472+RankKCodeTalker@users.noreply.github.com> Date: Thu, 16 Nov 2023 15:55:31 +0800 Subject: [PATCH 1/2] add slora (#174) --- .../public/assets/images/finetune/SLORA_1.png | Bin 0 -> 6597 bytes .../public/assets/images/finetune/SLORA_2.png | Bin 0 -> 240547 bytes .../public/assets/images/finetune/SLORA_3.png | Bin 0 -> 274123 bytes .../public/assets/images/finetune/SLORA_4.png | Bin 0 -> 118722 bytes .../public/assets/images/finetune/SLORA_5.png | Bin 0 -> 206390 bytes .../public/assets/images/finetune/SLORA_6.png | Bin 0 -> 112261 bytes .../public/assets/images/finetune/SLORA_7.png | Bin 0 -> 78629 bytes .../{rag => dataset}/information_retrieve.md | 0 src/zh/posts/finetune/SLORA.md | 53 ++++++++++++++++++ 9 files changed, 53 insertions(+) create mode 100644 src/.vuepress/public/assets/images/finetune/SLORA_1.png create mode 100644 src/.vuepress/public/assets/images/finetune/SLORA_2.png 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src/zh/posts/rag/information_retrieve.md rename to src/zh/posts/dataset/information_retrieve.md diff --git a/src/zh/posts/finetune/SLORA.md b/src/zh/posts/finetune/SLORA.md new file mode 100644 index 000000000..59e5471d4 --- /dev/null +++ b/src/zh/posts/finetune/SLORA.md @@ -0,0 +1,53 @@ +--- +author: 最后的开神-wkyc +icon: boxes-packing +date: 2023-11-16 +shortTitle: S-LoRA:Adapter的主存存储 +category: + - 微调技术 +tag: + - PEFT + - Hugging Face + - LoRA +--- + +# S-LoRA:为数千个并发LoRA Adapter提供服务 +S-LoRA 将所有Adapter存储在主存中,并将当前运行的查询使用的Adapter获取到 GPU 内存。为了有效利用GPU内存并减少碎片,S-LoRA提出了统一分页(Unified Paging)。 + +论文标题:S-LORA: SERVING THOUSANDS OF CONCURRENT LORA ADAPTERS +论文地址:https://arxiv.org/pdf/2311.03285.pdf +项目地址:https://github.com/S-LoRA/S-LoRA +## 1 介绍 +S-LoRA 是专为众多 LoRA Adapter的可扩展服务而设计的系统,它将所有Adapter存储在主存中,并将当前运行查询所使用的Adapter取到 GPU 内存中。 +S-LoRA 提出了「统一分页」(Unified Paging)技术,即使用统一的内存池来管理不同等级的动态Adapter权重和不同序列长度的 KV 缓存张量。此外,S-LoRA 还采用了新的张量并行策略和高度优化的定制 CUDA 内核,以实现 LoRA 计算的异构批处理。 +这些功能使 S-LoRA 能够以较小的开销在单个 GPU 或多个 GPU 上为数千个 LoRA Adapter提供服务(同时为 2000 个Adapter提供服务),并将增加的 LoRA 计算开销降至最低。相比之下,vLLM-packed 需要维护多个权重副本,并且由于 GPU 内存限制,只能为少于 5 个Adapter提供服务。 +与 HuggingFace PEFT 和 vLLM(仅支持 LoRA 服务)等最先进的库相比,S-LoRA 的吞吐量最多可提高 4 倍,服务的Adapter数量可增加几个数量级。因此,S-LoRA 能够为许多特定任务的微调模型提供可扩展的服务,并为大规模定制微调服务提供了潜力。 +S-LoRA 包含三个主要创新部分。论文的第 4 节介绍了批处理策略,该策略分解了 base 模型和 LoRA Adapter之间的计算。此外,研究者还解决了需求调度的难题,包括Adapter集群和准入控制等方面。跨并发Adapter的批处理能力给内存管理带来了新的挑战。第 5 节,研究者将 PagedAttention 推广到 Unfied Paging,支持动态加载 LoRA Adapter。这种方法使用统一的内存池以分页方式存储 KV 缓存和Adapter权重,可以减少碎片并平衡 KV 缓存和Adapter权重的动态变化大小。最后,第 6 节介绍了新的张量并行策略,能够高效地解耦 base 模型和 LoRA Adapter。 +## 2 批处理 +对于单个Adapter,Hu et al., 2021 推荐的方法是将Adapter权重合并到 base 模型权重中,从而得到一个新模型(见公式 1)。这样做的好处是在推理过程中没有额外的Adapter开销,因为新模型的参数数与 base 模型相同。事实上,这也是最初 LoRA 工作的一个突出特点。 +本文指出,将 LoRA Adapter合并到 base 模型中对于多 LoRA 高吞吐量服务设置来说效率很低。取而代之的是,研究者建议实时计算 LoRA 计算 xAB(如公式 2 所示)。 +在 S-LoRA 中,计算 base 模型被批处理,然后使用定制的 CUDA 内核分别执行所有A大平台而的附加 xAB。这一过程如图 1.1 所示。研究者没有使用填充和 BLAS 库中的批处理 GEMM 内核来计算 LoRA,而是实施了定制的 CUDA 内核,以便在不使用填充的情况下实现更高效的计算。 + +![图2.1 示意图](/assets/images/finetune/SLORA_2.png "图2.1 批处理示意图") + +## 3 Unified Paging +与为单个 base 模型提供服务相比,同时为多个 LoRA Adapter提供服务会带来新的内存管理挑战。为了支持多个Adapter,S-LoRA 将它们存储在主存中,并将当前运行批所需的Adapter权重动态加载到 GPU RAM 中。 + +![图3.1 示意图](/assets/images/finetune/SLORA_3.png "图3.1 内存管理示意图") + +在这个过程中,有两个明显的挑战。首先是内存碎片,这是由于动态加载和卸载不同大小的Adapter权重造成的。其次是Adapter加载和卸载带来的延迟开销。为了有效解决这些难题,研究者提出了 「Unfied Paging」,并通过预取Adapter权重将 I/O 与计算重叠。 +研究者将 PagedAttention 的想法扩展为统一分页(Unified Paging),后者除了管理 KV 缓存外,还管理Adapter权重。统一分页使用统一内存池来联合管理 KV 缓存和Adapter权重。为了实现这一点,他们首先为内存池静态分配一个大缓冲区,除了 base 模型权重和临时激活张量占用的空间外,该缓冲区使用所有可用空间。KV 缓存和Adapter权重都以分页方式存储在内存池中,每页对应一个 H 向量。因此,序列长度为 S 的 KV 缓存张量占用 S 页,而 R 级的 LoRA 权重张量占用 R 页。图 3 展示了内存池布局,其中 KV 缓存和Adapter权重以交错和非连续方式存储。这种方法大大减少了碎片,确保不同等级的Adapter权重能以结构化和系统化的方式与动态 KV 缓存共存。 + +![图3.2 示意图](/assets/images/finetune/SLORA_4.png "图3.2 Unified Paging示意图") + +## 4 张量并行 +此外,研究者为批量 LoRA 推断设计了新颖的张量并行策略,以支持大型 Transformer 模型的多 GPU 推断。张量并行是应用最广泛的并行方法,因为它的单程序多数据模式简化了其实施和与现有系统的集成。张量并行可以减少为大模型提供服务时每个 GPU 的内存使用量和延迟。在本文设置中,额外的 LoRA Adapter引入了新的权重矩阵和矩阵乘法,这就需要为这些新增项目制定新的分区策略。 + +![图4.1 示意图](/assets/images/finetune/SLORA_5.png "图4.1 张量并行示意图") + +## 5 评估 +最后,研究者通过为 Llama-7B/13B/30B/70B 提供服务来评估 S-LoRA。 + +![图5.1 示意图](/assets/images/finetune/SLORA_6.png "图5.1 评估结果") + +结果表明,S-LoRA 可以在单个 GPU 或多个 GPU 上为数千个 LoRA Adapter提供服务,而且开销很小。与最先进的参数高效微调库 Huggingface PEFT 相比,S-LoRA 的吞吐量最多可提高 30 倍。与使用支持 LoRA 服务的高吞吐量服务系统 vLLM 相比,S-LoRA 可将吞吐量提高 4 倍,并将服务Adapter的数量增加几个数量级。 From 9e9688bc598401cae3f23bd6a8f3c35fe684b6a3 Mon Sep 17 00:00:00 2001 From: sheli00 <44807582+sheli00@users.noreply.github.com> Date: Sat, 9 Dec 2023 19:04:35 +0800 Subject: [PATCH 2/2] docs: update reasoning lats (#175) --- .../public/assets/images/reasoning/lats1.png | Bin 0 -> 89461 bytes .../public/assets/images/reasoning/lats2.jpg | Bin 0 -> 104342 bytes .../public/assets/images/reasoning/lats3.jpg | Bin 0 -> 61274 bytes .../public/assets/images/reasoning/lats4.jpg | 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      6mD4D_}n3FM44};jMgj52${N^ zPb5b;@y|MW%l}>{#}W^mcdxP)%C8^J2yXTw5Bg0^d}hSc*aA9LVZ~8(V+9{_q%8`K z?0!DAiyNRj>EzmPN$P0*s+};=Ln4m(hir@7ou(rXb)`26DuSmp$6G2KIJlnvwF_9= z@xQDSD$U}}-l8-Whe*YoH<(>Ae#3=NLJ^gI43A+Eb)K(reG1&oerVnjT|Qr3EdU9* zUkbik&%)3|eQ}=LQ{=tHQ@{0-h)Awl!{k6OdCU0{5c@wreF?KzD&e#Vyz7qvY}n&M z>t;E^Ll}>$6zb_x_Ql;1)nR<(-j!OGoselMZ~vu(+r`8dWN_DGEYSXR;X7IFLZ|mx zX|m!qlbH!C6-J&O4wk^PxJM8*9RIjmlU-&UErub*@5tvzM6veMlxxkvzYmG2-nCq7bJ zAAXwo>YPppm&4Y#vHX@ohVTgJ#m`u*s zkhlZ%rJ1}*x**t#4im){6Bj8ERTy6pZWmecia=Lf&s-@3Me8%^IGCN>d=9xduCxlo zE-F=7q0$|@u-F?Hn8pdNu_EqkCsAHloWL6i_ePNJ8!Z^7u%$UTFUM~|^w{KeWF)V0 zQ@L-xyj~t2KVoQy1lhTqhgCg@vBYNZ4q&yZ=_6t=-P=8$@1Lg_xS$FF%XNR1wwXGaQ;H=XiM$vuv|Aef&Xim1x8 zU42MSt|h(6kr^L)nX>J4wQ6>V^F|Mu@uMOAz>}vMhIZk!V?m=ljW9zp=@8utr`pWI zJC5Bn!OuI(7%2k_k720;O$Ml<%+E{{P-L+%zn$BR8(oJ-k#f8^>73j10ns~0;Zcy? zKeI;XHzJSZ0gHH%2q?}}kLP?M;w(QOL-4JB8UdYW2jX7scya7Dv`2;`=-vC^y}VVG z8OV^XyTo+N6~hK3{5($8*vBi+uUL3xgupI#<&rpNq3E8*_v;SYJ|*lHdt&0P5MBhF z32*F4C-uLYvVD#2a&FwrC?(45ym{6sDkc^@)j|V*Pj$|qFvRc6={vdnmocHVWj<() zlH3V3<77~A8!iwS%fN(!3gO@-rgaG}kmdw#&1%9p4ds)q4IM@W&0yVpo6NSZ$LCx$ zZI0~OXYX0U@QDmn^|~a*ipaBc^*=>(3H3N>X*mmPyo|}(Hu`!v(i;#EK>a#XL8ai? z*kEn{a>L_dwGy!+YIMG{mtC65;KfyWa;56mH(nJeW;;~Qo)>0JMC9aIxq+wxn7|bo z#F9xv4qQqs)Nrw%7k#f!8t|O~Z+a5Ofh;E`W+4H#>(5V+;~I-7{QN|YRtKo%gU7iq S4aEN*a7l^FiNDFQ{GAd literal 0 HcmV?d00001 diff --git a/src/zh/posts/reasoning/LATS.md b/src/zh/posts/reasoning/LATS.md new file mode 100644 index 000000000..b6d09260c --- /dev/null +++ b/src/zh/posts/reasoning/LATS.md @@ -0,0 +1,86 @@ +--- +author: 猞猁-zlj +icon: wand-magic-sparkles +date: 2023-12-09 +shortTitle: "LATS(语言代理树搜索)" +category: + - 大模型推理 +tag: + - 推理 + - LLM + - CoT + - ToT + - 强化学习 +--- + +# LATS: 将语言模型中的推理、行动和规划统一起来 + +`LATS`是一种利用大型语言模型(LLMs)进行决策的框架,它将LLMs作为代理、价值函数和优化器,以增强决策能力。LATS采用蒙特卡罗树搜索作为模型,利用外部反馈的环境提供更加灵活和适应性的问题解决机制。LATS在HumanEval上使用GPT-4实现了94.4%的编程得分,在WebShop上使用GPT-3.5实现了平均得分75.9。 + + + +::: tip +论文题目:Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models +作者:Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, Yu-Xiong Wang +机构:University of Illinois at Urbana-Champaign +::: + + + +--- + +## 1 介绍 + +LLMs不仅在标准的自然语言处理任务上表现出色,还被应用于需要高级常识推理或量化技能的任务。LLMs还能在涉及知识和推理的复杂环境中表现出色,如网络导航、工具使用或开放式游戏。一些方法通过**外部反馈或观察**来增强LLMs的推理和行动能力,但这些方法缺乏人类的深思熟虑的决策特征。最近的搜索引导LLM方法通过**搜索多个推理链**来解决这个问题,但这些方法在孤立的情况下运行,不包括可以改善推理的外部反馈。 + +LATS是一个用于**决策和推理**的语言模型框架,通过在可能的推理和行动步骤的组合空间上进行搜索,将LLM规划、行动和推理策略统一起来。LATS利用预训练的LLM作为代理、价值函数和优化器,利用现代LLM的强大自然语言理解和上下文学习能力,使用文本作为框架各组件之间的接口,使LATS能够根据环境条件自适应规划,而无需额外训练。LATS是首个将推理、行动和规划结合起来增强LLMs的框架。LATS在HotPotQA和WebShop上的性能超过了ReAct,并在HumanEval上取得了最先进的结果。LATS通过LM的启发式指导,引入了基于LM的蒙特卡洛树搜索变体,从采样的动作中构建最佳轨迹,实现了比反射式提示方法更灵活和适应性更强的问题解决。LATS通过**整合外部反馈和自我反思**,增强了模型的感知能力,并使代理能够从经验中学习,超越了基于推理的搜索方法。通过在编程、交互式QA和网页导航等不同领域的实验,我们展示了LATS在利用LLMs进行自主推理和决策方面的多样性。 + +![](/assets/images/reasoning/lats1.png "图1.1 LATS的整体框架") + +在中心是LLM Agent,也就是语言模型,它起到整个系统的核心作用。左边是一个 Tree Search 模块,这实现了蒙特卡罗树搜索,用来有效地构建推理路径,实现决策规划。右边是Environment,也就是外部环境。Agent可以在这个环境中进行交互,产生 Actions 和收到 Observations。Agent 和 Environment 通过文本的形式进行交互,这样可以利用语言模型本身优秀的自然语言理解能力。在 Agent 内部引入了Context Memory,可以存储之前的语境,用于之后的决策更新。还有一个 Self-reflection 模块,当遇到失败情况时,可以提供文本反馈,指出错误原因并给出改进建议。整个系统的目标是利用语言模型本身的知识表达、推理和补全的能力,在与外部环境交互的过程中,通过误差学习、决策树搜索等方式进行自我完善,实现可靠的决策。最终找到 the Best Node,也就是推理/决策路径中最佳的节点,产生 model 输出。 + +## 2 和类似工作的对比 + +![](/assets/images/reasoning/lats2.jpg "图2.1 类似工作示意图") + +1. ToT和RAP仅依赖于语言模型的内部知识进行状态扩展和评估。而LATS引入了环境(Environment)的反馈。 + +2. 通过与外部环境的交互,LATS可以获得额外的观察和奖励信号,这些信号指导树搜索过程,并可用于增强语言模型的感知能力,避免事实hallucination(事实幻觉)。 + +3. ToT和RAP使用简单的搜索算法(BFS、DFS等),而LATS采用了更具原则性的蒙特卡洛树搜索,可以更好地平衡exploration和exploitation。 + +4. LATS还引入了自我反思(Self-Reflection)模块,语言模型可以从反思和经验中学习,不需要额外的训练优化。这种自我完善的能力也是ToT和RAP所没有的。 + +5. 综上,LATS是一个更为完整和统一的框架,它协同了规划、推理和行动三者的能力,可以更好地把语言模型作为决策代理开发和应用到复杂任务中。 + +## 3 统一规划、推理和行动 + +### 3.1 大预言模型智能体(LLM Agent) + +LATS是基于ReAct的支持顺序推理或决策任务的模型。该模型使用LM作为基础决策者,并通过采样多个动作来构建最佳轨迹。这种方法可以在决策和推理空间中进行更大的探索。 + +### 3.2 LATS + +![](/assets/images/reasoning/lats3.jpg "图3.1 LATS的工作流程") + +1. Selection(选择):使用UCT算法基于价值和访客次数选择树中最适合扩展的节点。从根节点开始,在每一层上选择UCT值最高的子节点,直到达到叶节点。 +2. Expansion(扩展):在选定要扩展的叶子节点上,使用语言模型pθ根据当前状态采样n个可能的操作。每个操作会影响环境并返回一个观察结果。这就添加了n个新的子节点到树中。 +3. Evaluation(评估):使用语言模型pV对新增的每个子节点进行标量评分,反映解决方案的进展程度。这些评分用作启发式信息来指导搜索算法。 +4. Simulation(模拟):从当前选择的节点开始扩展,采样高评分的节点直到达到终止节点,为整个解决方案轨迹提供反馈。 +5. Backpropagation(反向传播):使用模拟结果更新沿途所有节点的评分,为后续选择提供更好的估计。 +6. Reflection(反思):如果轨迹失败,使用语言模型p_{ref}生成文本反思,总结错误并提出改进建议。这些反思存储并用于增强代理。 +这些操作循环执行,直到完成任务或达到计算预算。LATS通过搜索、环境交互和反思协同工作,以增强语言模型的推理和决策能力。 + +## 4 实验 + +本文介绍了LATS方法在不同决策领域的应用。这些领域包括编程、HotPotQA和WebShop。LATS方法结合了推理和行动能力,具有广泛的适用性。 + +![](/assets/images/reasoning/lats4.jpg "图4.1 编程领域实验结果") + +本文研究了在编程任务中使用外部观察的重要性,通过在Humaneval和MBPP数据集上评估基线和LATS的表现,发现搜索和语义反馈对于更好的性能至关重要。LATS在两个数据集上的表现最好,RAP使用类似的搜索算法,也表明了外部反馈对于困难的推理任务(如编程)的重要性。使用LATS和GPT-4在HumanEval数据集上取得了最好的表现。 + +![](/assets/images/reasoning/lats5.jpg "图4.2 HotPotQA和WebShop实验结果") + +本文研究了在HotPotQA数据集上使用内部推理和外部检索策略的表现。现代LLMs已经编码了事实知识,可以直接回答问题。搜索方法ToT和RAP可以采样和探索更多输出,对需要推理的问题有更大的提升。LATS比ReAct表现更好,即使采样相同数量的轨迹,通过扩展更多节点进行有原则的搜索。在内部推理方面,LATS与RAP相当,但表现不如行动。将内部和外部推理结合起来,LATS的表现最好,表明外部反馈在增强推理方面的重要性。 + +本文介绍了一个复杂的决策环境WebShop,它是一个在线购物环境,由一个包含1.18M真实产品和12k人类指令的网站组成。文章使用预构建的搜索和点击命令以及浏览器反馈和反思作为观察。通过平均得分和成功率两个指标来评估性能。结果表明,GPT-3.5与ReAct在WebShop中表现出与模仿学习相当的竞争力,并且可以超越强提示策略的强化学习技术。使用LATS可以显著提高性能,表明它可以在相同的迭代次数下实现更有效的探索。 \ No newline at end of file

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