Jan 21, 2024 · Indirect Access KV Cache. There is a limited body of recent art di-rectly towards compressing the KV cache in LLM to mitigate the bottleneck. Now we will shift gears and explore the different performance May 24, 2024 · Figure 1: Accuracy and efficiency comparisons across various KV cache compression methods. Sat 9:55 a. In addition to the large number of parameters, the key-value (KV) cache for the attention mechanism in the transformer architecture consumes a significant amount of memory, especially when the number of layers is large for deep language models. First, since KV-cache is designed to leverage the causal attention map, we minimize computation and computation automatically. However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. KV cache in ChunkAttention is a pre-fix tree built with chunked context tokens and key/value tensors. It will be fetched again during the generation of the next token. It ensures efficient sequence generation by caching previously computed KV states. Quantization has emerged as an effective technique for KV cache Mar 14, 2024 · This paper introduces "Keyformer", an innovative inference-time approach, to mitigate the challenges associated with KV cache size and memory bandwidth utilization. In our experiments across various asks, FastGen demonstrates substantial reduction on GPU memory consumption with negligible generation quality loss. May 17, 2024 · addition to the large number of parameters, the. Foteini Strati, Sara Mcallister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic. (2023) propose to store only the KVs of initial and recent tokens in the KV cache. For every generated token, the KV-cache stores the attention key/value activations of each Transformer layer. Large language models (LLMs) excel in natural language processing but demand intensive computation. Yet, the key-value (KV Nov 17, 2023 · It also reduces the size of the KV-cache in memory, allowing space for larger batch sizes. The KV cache only stores key/value tensors of se- We would like to show you a description here but the site won’t allow us. 855 seconds. Sep 18, 2023 · A large KV cache is thus a limitation when dealing with LLM inference. As the batch size, context length, or model size increases, the size of the key and value (KV) cache can quickly become the main contributor to GPU memory usage and the bottleneck of inference latency. Although recent methods were proposed Key-value cache, or kv cache, is needed to optimize the generation in autoregressive models, where the model predicts text token by token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42. • Training Memory Footprint: CLA reduces the memory footprint of intermediate KV May 21, 2024 · The memory footprint of the key-value (KV) cache can be a bottleneck when serving large language models (LLMs). 55% higher accuracy than GQA. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42. Moreover, with the lightweight attention profiling used to guide the construction of the adaptive KV cache, FastGen can be deployed without resource-intensive fine-tuning or re-training. Jan 2, 2024 · The Recipe of StreamingLLM. 885 +- 0. One is that reallocating (and copying!) the kv-cache every time the . KV cache works seamlessly with the self-attention layer. 2 days ago · LongCache concatenates the selected KV cache segments between the global and local parts, and then performs positional embedding sequentially, ignoring the absolute distance between the selected segments, while only preserving the relative order, as shown in Figure 1(b). -Richard Feynman. PyramidInfer can reduce over 54% GPU memory usage in the KV cache while having more than 2x throughput. May 8, 2024 · Hence, KV-Runahead parallelizes the prompt phase by orchestrating multiple processes to populate the KV-cache and minimizes the time-to-first-token (TTFT). 探索知乎专栏，发现有趣的人和故事。 These optimizations include, but are not limited to, caching, scheduling, compression, and offloading. Oct 3, 2023 · Moreover, with the lightweight attention profiling used to guide the construction of the adaptive KV cache, FastGen can be deployed without resource-intensive fine-tuning or re-training. We propose a data-free distillation method that leverages generations produced by These solutions require a fast and versatile KV cache stream-ing mechanism. In LLaMA, the KV-cache tensor slices are updated in-place; this leads to recompilation Mar 4, 2024 · DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. Without cache, the model computes the M hidden states for the input, then generates a first output token. Larger mod-els, batch sizes, or longer generated sequences lead to a larger KV cache memory footprint. key-value (KV) cache for the attention mecha-. m. Here are his words: "I'm working on some benchmarks at the moment, but they're taking a while to run. 272 seconds. The solution is the KV cache Jan 31, 2024 · Implemented in 2 code libraries. when the Dec 22, 2023 · When we KV cache, actual inputs to the model are the last generated token (vs. GQA, for a similar KV-cache size, QCQA provides 10. In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. FastGen [Ge et al. We conduct a comprehensive evalua-tion of MiniCache utilizing various models including LLaMA-2, LLaMA-3, Phi-3, Mistral, and Mixtral across multiple benchmarks, demonstrating its exceptional As a result, we are able to distill the 7B, 13B and 30B LLaMA models with weights and KV cache quantized down to 4-bits. KVCache. Efficiently serving large language models (LLMs) requires batching many requests together to reduce the cost per request. 5% of training costs, reduces the KV cache by 93. Based on this structure, the adaptive KV cache is constructed Jun 24, 2024 · View a PDF of the paper titled Training-Free Exponential Extension of Sliding Window Context with Cascading KV Cache, by Jeffrey Willette and 4 other authors View PDF HTML (experimental) Abstract: The context window within a transformer provides a form of active memory for the current task, which can be useful for few-shot learning and Jan 3, 2024 · The Role of KV Cache. What I cannot create, I do not understand. While these methods help mitigate the pressure on the scarce GPU memory from using KV cache, offloading KV cache to CPU/NVMe can still add non-trivial overhead to generative inference performance due to the limited PCIe bandwidth between the GPU FP8 E5M2 KV Cache# The int8/int4 quantization scheme requires additional scale GPU memory storage, which reduces the expected GPU memory benefits. Different from training, models in the inference do not need to record the optimizer states, activations, or gradients. Assuming model dimension as D and number of attention heads as H, dimensions per head is D/H. Dual-purposing the KV-cache scheme has two main benefits. Mar 7, 2024 · In this paper, we propose QAQ, a quality adaptive quantization scheme for KV cache in LLMs. e. Most LLM inference frameworks preallocate GPU memory for the KV cache This is called KV cache, and it may take up a large amount of memory for large models and long sequences. 知乎专栏提供一个平台，让用户随心所欲地写作和自由表达观点。 Oct 12, 2023 · Incorporating the KV Cache allows the inference process of LLM to be viewed as two stages. To address this problem, this paper introduces SnapKV, an innovative and fine-tuning-free Aug 3, 2023 · Attention KV cache can be quantized and compressed for storage, to get a higher sample throughput. - 10:00 a. Inference cost from the attention mechanism scales quadratically with input sequence length. rence for Large Lan-guage Models (LLMs). For every subsequent iteration, you only need to compute the key, query, and value vector May 21, 2024 · Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs). In addition to quantizing weights 015 and activations, we also quantize the KV cache, 016 which is critical for increasing throughput and 017 support long sequence dependencies at current 018 model sizes. Static kv-cache and torch. For a batch size of 512 and context length of 2048, the KV cache totals 3TB, that is 3x the model size (!). The difference in inference speed was huge while the GPU VRAM usage was neglectable, as reported here, so The KV cache size depends on the number of layers, hid-den units per layer, floating point precision, batch size, and sequence length tokens (Sheng et al. In training the whole sequence is processed at once (therefore KV cache memory = 0) Activation Memory = In forward pass every operation's output has to be stored for doing . This research paper was presented at the 12th International Conference on Learning Representations (ICLR 2024), the premier conference dedicated to the advancement of deep learning. We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once. However, inferring these models is extremely resource intensive due to the large size of the KV cache that must be stored throughout inference. 挚簇润和簸进 沾萝妙盖. This is useful as a drop-in replacement, if you want to debug or profile without the serialization or IO overheads. In the KV cache, the output of the model from previous time step is appended to the cache of key and value matrices of the current time step but the query matrix is updated at each time step to generate the next token. The reduction in key-value heads comes with a potential accuracy drop. Feb 14, 2024 · Many computational factors limit broader deployment of large language models. Preliminary results show the Q4 cache mode is more precise overall than FP8, and comparable to full precision. It consists of two components, i. in Efficient Memory Management for Large Language Model Serving with PagedAttention, the current trend in the GPU market is characterized by a stable growth in the computation speed (FLOPS) and a much slower increase of the memory May 7, 2024 · Efficient deployment of Large Language Models (LLMs) requires batching multiple requests together to improve throughput. In these models, generating tokens one by one is a common practice, but it can be computationally expensive because it repeats certain calculations at each step . In autoregressive decoding, Q vector is generated, and cached values of K and V matrices are fetched. The self-decoder efficiently encodes global key-value (KV) caches that are An intuitive approach, known as window attention (Beltagy et al. Thus, the KV cache is prefix-aware and can dynamically detect and remove re-dundancy at runtime without human involvement. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Oct 6, 2023 · This study introduces a method called adaptive KV cache compression, which reduces the memory usage of generative inference for Large Language Models (LLMs). Jul 3, 2024 · It is quite vital to understand such anomaly, because: 1) it impacts the efficiency of KV cache compression [9], a crucial technique for deploying large models in resource-constrained environments. The common optimization trick for speeding up transformer inference is KV caching 1 2. We are going to build everything end-to-end from basics to a functioning web app similar Apr 22, 2024 · Large Language Models (LLMs) have made remarkable progress in processing extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their performance. While these methods help mitigate the pressure on the scarce GPU memory from using KV cache, offloading KV cache to CPU/NVMe can still add non-trivial overhead to generative inference performance due to the limited PCIe bandwidth between the GPU Moreover, with the lightweight attention profiling used to guide the construction of the adaptive KV cache, FastGen can be deployed without resource-intensive fine-tuning or re-training. Grouped Query Attention, Rotary Embedding, KV Cache, Root Mean Square Normalization. In this paper, we propose to reduce the mem-ory consumption of the KV cache StreamingLLM would let the model function continuously, basing its responses on recent conversations without needing to refresh its cache. Jan 29, 2024 · When memory usage exceeds GPU capacity, the generative inference of LLMs typically resort to offloading (Aminabadi et al. 4. 197 +- 1. Then each of the heads will maintain its own separate KV cache: Sep 2, 2020 · The cache is only used for generation, not for training. backward() . , 2022; Sheng et al. nism in the transformer architecture consumes. In addition, the full KV cache is also considered as strong baseline to measure the performance loss of other methods. (2024);Han et al. Apr 18, 2024 · They have improved pretraining and post-training processes, resulting in reduced false refusal rates, better alignment, and more diverse responses from the model. Next, we describe our KV cache streaming library, D ́ej`aVuLib (§4. Existing studies [9] have leveraged attention distribution patterns to develop adaptive KV cache compression methods, significantly reducing memory Feb 7, 2023 · Hello! 👋 I’m benchmarking inference performance using Whisper and the . Different from the conventional KV cache that retains key LLM（澜种）：挂崎 KV Cache 错谭报另，纱诺停合 StreamingLLM. My understanding is that when using the cache, inference should be faster (since we don’t recompute k-v states and cache them instead), but VRAM usage higher (since we keep the cached tensors in memory). The project focuses on developing effective and practical techniques that enhance both academic research and open-source development. Amirkeivan Mohtashami · Matteo Pagliardini · Martin Jaggi 🔗. It can be directly trained like a GPT (parallelizable). KV (Key/Value) Caches are a crucial optimization technique in the generation phase of auto-regressive models like GPT, used within the multi-head attention (MHA) mechanism. a significant amount of memory, especially. They are particularly important in speeding up the generation phase by storing pre-computed key (K) and value (V) elements. However, I’m finding that when using cache Oct 7, 2023 · Here, for generating the word deep, we feed only the uses word into the model and fetch the representation of Large language models are recent advances in deep learning, which from the cache. LLaMA 2. into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Specifically, the KV cache manager manages the physical KV cache memory on the GPU workers through the instructions sent by the centralized scheduler. Low parallelizability. Oct 17, 2023 · Key-Value (KV) caching is a technique used to accelerate the inference process in machine learning models, particularly in autoregressive models like GPT and Llama. In this regard, our approach exhibits significant enhancements in quality compared to post-training quantization. 1), and how our system, D ́ej`aVu, implements the proposed solutions using D ́ej`aVuLib. speedup. We theoretically derive the connection between recurrence and attention. Existing work has proposed various methods for decreasing the memory footprint of the KV cache, such as storing KV activations in low precision, evicting unimportant KV cache entries, and sharing keys and values across query heads in the attention mechanism. , even just evicting the KV of the first token, as The KV cache technique is only used during the inference process and not during the training process. KV cache manager efectively manages the KV cache in a paged fashion, enabled by PagedAttention. Reduction in the KV cache can directly result in a larger batch size. LLMs are seeing growing use for applications such as document analysis and summarization which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to memory consumption during inference. Unlike the conventional KV cache that stores key and value vectors for all context tokens, this method uses targeted profiling to understand the structure of attention modules. Working of KV cache: Suppose we have n transformer layers in the architecture. 76 times. It is called key-value (KV) cache. Quantization is a commonly employed method for compressing model sizes and has been widely utilized for the compression of weights and activations Zhu et al. Zhang et al. CL] 29 Jan 2024MODEL TELLS YOU WHAT TO DISCAR. Jun 5, 2024 · LLM consumes huge GPU memory in the KV cache compared to the small model. While existing KV cache methods approach this problem by pruning or evicting large swaths of relatively less important KV pairs to dramatically reduce Mar 8, 2024 · Turboderp, developer of Exllama V2 has made a breakthrough: A 4 bit KV Cache that seemingly performs on par with FP16. May 2, 2024 · In a typical generation loop, after the prompt is processed in a single forward step, a sequence length of 1 (next token predicted) is fed into the forward pass of the model along with the kv-cache. 6\\times}$ less peak memory usage (including the model weight). We refer to the Llama-based model with dual chunk attention as ChunkLlama. In our experiments across various asks, FastGen demonstrates substantial May 8, 2024 · LLM profiling guides KV cache optimization. Since the invention of the transformer, two of the most effective interventions discovered for reducing the size of the KV Jan 7, 2024 · Key-Value Cache # Because key and value matrices are for maintaining context, matrices that are generated in previous iterations can be cached and reused, instead of calculating the entire K/V from scratch. (2023) propose a dynamic KV cache eviction policy to only keep a small portion of the KV cache in memory. However, the memory footprint of the KV cache poses a critical bottleneck in LLM deployment as the cache size grows with batch size and sequence length, often surpassing even the size of the model itself. , a cross-decoder stacked upon a self-decoder. Large Language Models (LLMs) have had a profound impact on AI applications, particularly in the domains of long-text comprehension and generation. RWKV is an RNN with transformer-level LLM performance. This is problematic for two reasons. g. The paper should be inserted in the correct position in chronological order (publication/arxiv release time). In the first iteration, the KV Cache is empty, so we need to compute all the key, query, and value vectors for these tokens, and we will cache the key/value vectors. a theoretical guarantee justifies that such a compressed KV cache can approximate the attention output. 1 Introduction Feb 2, 2024 · LLM Inference Series: 5. the whole sequence) and the KV cache. In this paper, we propose a novel method that only computes and caches the KVs of a small number of Apr 24, 2024 · Since CORM reduces KV cache without need for training, we consider several similar approaches as our baselines: StreamLLM , Scissorhands and H 2 O . Moreover, as pointed out by Kwon et al. We May 21, 2024 · LLM consumes huge GPU memory in the KV cache compared to the small model. In Section 5, we empirically evaluate SCISSORHANDS and show that SCISSORHANDS reduces the memory usage of KV cache up to 5×without degradation on model quality. Because the size of the KV cache scales proportionally with both sequence length and batch size, the memory overhead of KV cache storage can limit batch sizes when operating on long sequence lengths (Chowdhery et al. The D ́ej`aVu LLM serving system. To illustrate this concept, see the inner workings of the MaskedSelfAttention operator in the figure below. Earlier methods would either need a cache reset when the conversation length exceeded the training length (losing recent context) or recompute KV states from recent text history, which can be time-consuming. Our MiniCache is training-free and general, complementing existing KV cache compression strategies, such as quantization and sparsity. Jan 10, 2023 · The KV cache should be stored in memory during decoding time; E. PagedAttention attempts to optimize memory use by partitioning the KV cache into blocks that are accessed through a lookup table. Existing approaches to reduce the KV cache size involve either fine-tuning the model to learn a compression strategy or leveraging attention scores to reduce the sequence length. • KV Cache Memory: CLA significantly reduces KV cache memory footprint, shrinking it by a factor equal to the sharing factor, or slightly less if the sharing factor does not evenly divide the number of layers. LLM101n: Let's build a Storyteller. AI is a collaborative endeavor with leading industry partners such as Approaching. kv_cache is used to reduce computation for decoder layer but it also brings memory overheads. The parameters of 'use_cache_quantization' and 'use_cache_kernel' are provided to control kv-cache-quantization behavior When use_cache_quantization=True and use_cache_kernel=True, kv-cache-quantization will be enabled. Mar 27, 2024 · However, most of these techniques require fine-tuning and even pre-training in some cases. A few days ago, I read an awesome blog post on GPT in 60 Lines of NumPy. , 2020) (Figure 1 b), maintains only a fixed-size sliding window on the KV states of most recent tokens. That means, to predict token number 1000 in the generation, you May 6, 2024 · It comprises 236B total parameters, of which 21B are activated for each token. Remarkably, LLaMA-13B outperforms the colossal GPT-3 (175B) despite being just a fraction of its handling extremely long contexts, compressing the KV cache becomes pronounced. compile. , 2023). By Liyuan Liu , Senior Researcher Jianfeng Gao , Distinguished Scientist & Vice President. Hand in hand, you'll be able create, refine and illustrate little stories with the AI. CoTFormer: More Tokens With Attention Make Up For Less Depth ( Contributed Talk & Poster ) > link. Pivotal Function of KV Cache within Transformer’s Architecture. Distributed LLM serving is costly and often underutilizes hardware accelerators due to three key challenges: bubbles in pipeline-parallel deployments caused by the bimodal latency of KV-caching: # The transformers-neuronx library implements KV-cache optimization, which saves compute resources by reusing previously calculated SelfAttention key-value pairs, instead of recalculating them for each generated token. Such outliers are found to allocate most of the attention scores on initial tokens of input, termed as pivot tokens, which Feb 28, 2024 · When memory usage exceeds GPU capacity, the generative inference of LLMs typically resort to offloading (Aminabadi et al. , 2022), and can require employing costly techniques like offloading May 8, 2024 · You Only Cache Once: Decoder-Decoder Architectures for Language Models. 0, the trailblazing creation from Meta AI, stormed into the AI scene as one of the first high-performing and open-source pre-trained Language Model Models. This is all implemented in this gist which can be used as a drop-in replacement for Jun 28, 2023 · LLaMA implements autoregressive decoding with KV-cache. This paper unveils a previously overlooked type of outliers in LLMs. Among these methods, ZipCache achieves the highest accuracy, generation speed and compression ratio. We conduct a comprehensive evaluation of MiniCache utilizing various models including LLaMA-2, LLaMA-3, Phi-3, Mistral, and Mixtral across multiple benchmarks, demonstrating its exceptional performance in Feb 28, 2024 · Key-Value (KV) Caching has become an essential technique for accelerating the inference speed and throughput of generative Large Language Models~(LLMs). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding. KV cache for attention heads that broadly attend to all tokens. compact ones to reduce the KV cache length. Dissecting model performance. 1. We find that these methods break down at lower bit precision, and investigate quantization aware training for LLMs (LLM-QAT) to push quantization levels even further. This technique is so prominent that huggingface library has use_cache flag is enabled by default 6. 013 training data, similar to post-training quantiza-014 tion methods. We experiment with LLaMA mod-019 els of sizes 7B, 13B, and 30B, at Cache is training-free and general, complementing existing KV cache compression strategies, such as quantization and sparsity. comABSTRACTIn this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inf. Xiao et al. Figure 5 illustrates the D ́ej`aVu system. . However, as we generate more tokens, the “logical length” of the kv-cache grows. Link. May 29, 2023 · Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. , 2023] develops an adap-tive compression method for the KV cache, leveraging the observation that abundant structures exist in The paper or tools is related to Large Language Models (LLMs). If the compression algorithms or tools are only evaluated on small-scale language models (e. In the previous post, we took a deep dive into KV cache optimizations. Say you have M input tokens and want to generate N out put tokens. However, the growth of the KV cache in response to increasing input length poses challenges to memory and time efficiency. ,2023). 探讨KV Cache技术在大模型性能优化中的作用，包括对Self-Attention层和MLP层计算量的影响。 The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Next, We describe the PagedAttention algorithm in §4. In this course we will build a Storyteller AI Large Language Model (LLM). May 7, 2024 · DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. In this paper, we focus on a memory bottleneck imposed by the key-value (KV) cache, a computational shortcut that requires storing previous KV pairs during decoding. Self-attention can then be applied to the concatenated KV cache. KV Cache. The FP8 data format retains 2~3 mantissa bits and can convert float/fp16/bflaot16 and fp8 to each other. Apr 28, 2024 · Efficient LLM Inference with Kcache. During decoding, a LLM computes the key-value (kv) values for each input token and since it is autoregressive, it computes the same kv values each time because the generated output becomes part of the input now. Then, it computes the hidden state for the first generated token, and generates a second one. Furthermore, QCQA requires 40% less KV-cache size than GQA to attain similar accuracy. Data is collected with LLaMA3-8B model on Line Retrieval dataset. For example, when we use beam search, the kv_cache should be reordered according to latest beam idx and the current key/value should also be concat with kv_cache in the attention layer to get entire context to do scale dot Large World Model (LWM) is a recent work that enables training long context length models with up to 1M context length. May 28, 2024 · Reducing Transformer Key-Value Cache Size with Cross-Layer Attention. A arXiv:2310. Utilize a Sliding Window KV: This approach helps stabilize the model’s behavior over extended texts. without KV caching: 56. Keyformer leverages the observation that approximately 90% of the attention weight in generative inference focuses on a specific subset of tokens, referred to as "key" tokens. Thus, upon decoding a new token, the key/values of prior tokens no longer need recomputation. In TensorRT-LLM, each Transformer layer May 8, 2024 · The KV cache for the rest of the layers are on the CPU which usually has much larger capacity. This is not very efficient because you’re recomputing the same kv values each time. AI and Moonshot AI. 目彪 Decoder-base 恍衣扭旺度广（淌 GPT鸣 Oct 8, 2023 · with KV caching: 11. For example if you do output = Q * input where Q = (dim, dim) and input = (batch, seq, dim) then output of shape (batch, seq, dim) will need to be stored (in fp16). Additionally, models that need to leverage this optimization at inference need to train (or at least fine-tuned with ~5% of training volume) with MQA enabled. KV Cache technology is one of the most widely used techniques in the industry. 01801v3 [cs. 甸胯耐离 . This process can be slow since the model can generate only one token at a time, and each new prediction is dependent on the previous context. Figure 3 below provides an illustrated example of this new way of running the import {createMockCache} from 'kv-cache'; const cache = createMockCache (); Presents a similar API to the file cache, however it will immediately resolve all promises with null . 锅 Transformer 循 Encoder-base 蜒飞荚（奈 BERT翅氏）岁，能德杠盘倍傻循埋络弦恼筹街儡趋哨（簇香剑栖腥豫催疼乾员斜孽钉粪）。. 3%, and boosts the maximum generation throughput to 5. iu,minjiaz,jfgao}@microsoft. Details can be found in the supplementary material. The proposed quality and capacity-aware grouping of query heads can serve as a new paradigm for KV-cache optimization in autoregressive LLM inference. We introduce Keyformer, a novel token inference-time discarding technique to reduce KV cache size to Feb 5, 2024 · A tuning-free 2bit KV cache quantization algorithm, named KIVI, which can enable Llama (Llama-2), Falcon, and Mistral models to maintain almost the same quality while using $\\mathbf{2. To mitigate this, various quantization methods have been explored, yet they compromise LLM performance. generate() method, switching between using/not using the k-v cache). Although it ensures constant memory usage and decoding speed after the cache is initially filled, the model collapses once the sequence length exceeds the cache size, i. aware KV cache (PAKV) and two-phase parti-tion (TPP). (2023). Apr 30, 2024 · Retentive Network: A Successor to Transformer for Large Language Models. Feb 12, 2023 · gpt. Dual chunk attention is a training-free and effective method for extending the context window of large language models (LLMs) to more than 8x times their original pre-training length. Model calculates a new column for the K matrix and a new row for the V matrix. Once the forward pass is done with a layer, the corresponding KV cache is evicted back to the CPU. However, compressing the KV cache remains a challenging task. , BERT), they should not be included in the list. Nov 30, 2023 · The kv-cache is an inference-time optimization that caches the activations computed for the previous tokens (see here for a more in-depth explanation). StreamingLLM introduces a straightforward yet effective recipe for managing LLMs in streaming contexts: Maintain Attention Sinks: Always include several initial tokens as attention sinks in the KV cache. In a naive speculative decoding implementation, each speculative head would have its own kv-cache, but instead we modify the paged attention kernel Johannes Hagemann · Samuel Weinbach · Konstantin Dobler · Maximilian Schall · Gerard de Melo 🔗. Thus, the KV cache does not need to be stored in contiguous memory, and blocks are allocated as needed. om hl kg fo de jy dn mu oa po