Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM's in-context learning ability, or do not yield wall-clock time speedup on modern hardware. We hypothesize that contextual sparsity, which are small, input-dependent sets of attention heads and MLP parameters that yield approximately the same output as the dense model for a given input, can address these issues. We show that contextual sparsity exists, that it can be accurately predicted, and that we can exploit it to speed up LLM inference in wall-clock time without compromising LLM's quality or in-context learning ability. Based on these insights, we propose DejaVu, a system that uses a low-cost algorithm to predict contextual sparsity on the fly given inputs to each layer, along with an asynchronous and hardware-aware implementation that speeds up LLM inference. We validate that DejaVu can reduce the inference latency of OPT-175B by over 2$\times$ compared to the state-of-the-art FasterTransformer, and over 6$\times$ compared to the widely used Hugging Face implementation, without compromising model quality. The code is available at https://github.com/FMInference/DejaVu.

Large language models(LLMs) have sparked a new wave of exciting AI applications. Hosting these models at scale requires significant memory resources. One crucial memory bottleneck for the deployment stems from the context window. It is commonly recognized that model weights are memory hungry; however, the size of key-value embedding stored during the generation process (KV cache) can easily surpass the model size. The enormous size of the KV cache puts constraints on the inference batch size, which is crucial for high throughput inference workload. Inspired by an interesting observation of the attention scores, we hypothesize the persistence of importance: only pivotal tokens, which had a substantial influence at one step, will significantly influence future generations. Based on our empirical verification and theoretical analysis around this hypothesis, we propose scissorhands, a system that maintains the memory usage of the KV cache at a fixed budget without finetuning the model. In essence, Scissorhands manages the KV cache by storing the pivotal tokens with a higher probability. We validate that scissorhands reduces the inference memory usage of the KV cache by up to 5$\times$ without compromising model quality. We further demonstrate that scissorhands can be combined with 4-bit quantization, traditionally used to compress model weights, to achieve up to 20$\times$ compression.

Federated learning (FL) is a machine learning paradigm where multiple client devices train models collaboratively without data exchange. Data heterogeneity problem is naturally inherited in FL since data in different clients follow diverse distributions. To mitigate the negative influence of data heterogeneity, we need to start by measuring it across clients. However, the efficient measurement between distributions is a challenging problem, especially in high dimensionality. In this paper, we propose a one-pass distribution sketch to represent the client data distribution. Our sketching algorithm only requires a single pass of the client data, which is efficient in terms of time and memory. Moreover, we show in both theory and practice that the distance between two distribution sketches represents the divergence between their corresponding distributions. Furthermore, we demonstrate with extensive experiments that our distribution sketch improves the client selection in the FL training. We also showcase that our distribution sketch is an efficient solution to the cold start problem in FL for new clients with unlabeled data.

Given a single scene image, this paper proposes a method of Category-level 6D Object Pose and Size Estimation (COPSE) from the point cloud of the target object, without external real pose-annotated training data. Specifically, beyond the visual cues in RGB images, we rely on the shape information predominately from the depth (D) channel. The key idea is to explore the shape alignment of each instance against its corresponding category-level template shape, and the symmetric correspondence of each object category for estimating a coarse 3D object shape. Our framework deforms the point cloud of the category-level template shape to align the observed instance point cloud for implicitly representing its 3D rotation. Then we model the symmetric correspondence by predicting symmetric point cloud from the partially observed point cloud. The concatenation of the observed point cloud and symmetric one reconstructs a coarse object shape, thus facilitating object center (3D translation) and 3D size estimation. Extensive experiments on the category-level NOCS benchmark demonstrate that our lightweight model still competes with state-of-the-art approaches that require labeled real-world images. We also deploy our approach to a physical Baxter robot to perform grasping tasks on unseen but category-known instances, and the results further validate the efficacy of our proposed model. Code and pre-trained models are available on the project webpage11Project webpage. https://hetolin.github.io/SAR-Net.

Machine learning models are often deployed in settings where they must be constantly updated in response to the changes in class definitions while retaining high accuracy on previously learned definitions. A classical use case is fraud detection, where new fraud schemes come one after another. While such an update can be accomplished by re-training on the complete data, the process is inefficient and prevents real-time and on-device learning. On the other hand, efficient methods that incrementally learn from new data often result in the forgetting of previously-learned knowledge. We define this problem as Learning with Dynamic Definition (LDD) and demonstrate that popular models, such as the Vision Transformer and Roberta, exhibit substantial forgetting of past definitions. We present the first practical and provable solution to LDD. Our proposal is a hash-based sparsity model \textit{RIDDLE} that solves evolving definitions by associating samples only to relevant parameters. We prove that our model is a universal function approximator and theoretically bounds the knowledge lost during the update process. On practical tasks with evolving class definition in vision and natural language processing, \textit{RIDDLE} outperforms baselines by up to 30\% on the original dataset while providing competitive accuracy on the update dataset.

On-device localization and tracking are increasingly crucial for various applications. Machine learning (ML) techniques are widely adopted along with the rapidly growing amount of data. However, during training, almost none of ML techniques incorporate the known structural information such as floor plan, which can be especially useful in indoor or other structured environments. The problem is incredibly hard because the structural properties are not explicitly available, making most structural learning approaches inapplicable. We study our method through the intuitions from manifold learning. Whereas existing manifold methods utilizes neighborhood information such as Euclidean distances, we quantize the output space to measure closeness on the structure. We propose Neighbor Oblivious Learning (NObLe) and demonstrate our approach's effectiveness on two applications, Wi-Fi-based fingerprint localization and inertial measurement unit(IMU) based device tracking. We show that NObLe gives significant improvement over state-of-art prediction accuracy.

Network traffic anomaly detection is an important research content in the field of network and security management. By analyzing network traffic, the health of the network environment can be intuitively evaluated. In particular, analyzing network traffic provides practical and effective guidance for identification and classification of anomaly. This paper proposes a network traffic anomaly detection method based on wavelet analysis for pcap files contain two different delay injections. The wavelet analysis can effectively extract information from the signal and is suitable for the detection of anomaly. Firstly, wavelet analysis is used to extract the waveform features, and then the support vector machine is used for classification. In particular, packet lengths in the pcap files is parsed out to form a sequence of packet lengths in chronological order. Then followed by the wavelet analysis based packet length sequence feature extraction and feature selection methods, the resulting eigenvectors are used as input features to support vector machine for training the classifier. Thus to differentiate the two types of anomaly in the mixed traffic with both normal and abnormal traffic. The qualitative and quantitative experimental results show that our approach achieves good classification results.