RecycleGPT: An Autoregressive Language Model with Recyclable Module

Existing large language models have to run K times to generate a sequence of K tokens. In this paper, we present RecycleGPT, a generative language model with fast decoding speed by recycling pre-generated model states without running the whole model in multiple steps. Our approach relies on the observation that adjacent tokens in a sequence usually have strong correlations and the next token in a sequence can be reasonably guessed or inferred based on the preceding ones. Experiments and analysis demonstrate the effectiveness of our approach in lowering inference latency, achieving up to 1.4x speedup while preserving high performance.

A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting

To enhance predicting performance while minimizing computational demands, this paper introduces a joint time-frequency domain Transformer (JTFT) for multivariate forecasting. The method exploits the sparsity of time series in the frequency domain using a small number of learnable frequencies to extract temporal dependencies effectively. Alongside the frequency domain representation, a fixed number of the most recent data points are directly encoded in the time domain, bolstering the learning of local relationships and mitigating the adverse effects of non-stationarity. JTFT achieves linear complexity since the length of the internal representation remains independent of the input sequence length. Additionally, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies and prevent performance degradation due to the entanglement of temporal and channel-wise modeling. Experiments conducted on six real-world datasets demonstrate that JTFT outperforms state-of-the-art methods.

NAMSG: An Efficient Method For Training Neural Networks

We introduce NAMSG, an adaptive first-order algorithm for training neural networks. The method is efficient in computation and memory, and is straightforward to implement. It computes the gradients at configurable remote observation points, in order to expedite the convergence by adjusting the step size for directions with different curvatures in the stochastic setting. It also scales the updating vector elementwise by a nonincreasing preconditioner to take the advantages of AMSGRAD. We analyze the convergence properties for both convex and nonconvex problems by modeling the training process as a dynamic system, and provide a guideline to select the observation distance without grid search. A data-dependent regret bound is proposed to guarantee the convergence in the convex setting. Experiments demonstrate that NAMSG works well in practical problems and compares favorably to popular adaptive methods, such as ADAM, NADAM, and AMSGRAD.

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

This paper reports our efforts on swCaffe, a highly efficient parallel framework for accelerating deep neural networks (DNNs) training on Sunway TaihuLight, the current fastest supercomputer in the world that adopts a unique many-core heterogeneous architecture, with 40,960 SW26010 processors connected through a customized communication network. First, we point out some insightful principles to fully exploit the performance of the innovative many-core architecture. Second, we propose a set of optimization strategies for redesigning a variety of neural network layers based on Caffe. Third, we put forward a topology-aware parameter synchronization scheme to scale the synchronous Stochastic Gradient Descent (SGD) method to multiple processors efficiently. We evaluate our framework by training a variety of widely used neural networks with the ImageNet dataset. On a single node, swCaffe can achieve 23\%\~{}119\% overall performance compared with Caffe running on K40m GPU. As compared with the Caffe on CPU, swCaffe runs 3.04\~{}7.84x faster on all the networks. Finally, we present the scalability of swCaffe for the training of ResNet-50 and AlexNet on the scale of 1024 nodes.