Parallelizing Autoregressive Generation with Variational State Space Models

• URL: http://arxiv.org/abs/2407.08415v1
• Date: Thu, 11 Jul 2024 11:41:29 GMT
• Title: Parallelizing Autoregressive Generation with Variational State Space Models
• Authors: Gaspard Lambrechts, Yann Claes, Pierre Geurts, Damien Ernst,
• Abstract summary: We propose a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. The decoder recurrence allows generation to be resumed without reprocessing the whole sequence.
• Score: 6.29143368345159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. Moreover, the decoder recurrence allows generation to be resumed without reprocessing the whole sequence. Finally, we propose the autoregressive VSSM that can be conditioned on a partial realization of the sequence, as is common in language generation tasks. Interestingly, the autoregressive VSSM still enables parallel generation. We highlight on toy problems (MNIST, CIFAR) the empirical gains in speed-up and show that it competes with traditional models in terms of generation quality (Transformer, Mamba SSM).

Related papers

• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• Longhorn: State Space Models are Amortized Online Learners [51.10124201221601]
  State-space models (SSMs) offer linear decoding efficiency while maintaining parallelism during training. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. We introduce a novel deep SSM architecture, Longhorn, whose update resembles the closed-form solution for solving the online associative recall problem.
  arXiv  Detail & Related papers  (2024-07-19T11:12:08Z)
• The Expressive Capacity of State Space Models: A Formal Language Perspective [0.8948475969696075]
  recurrent models based on linear state space models (SSMs) have shown promising performance in language modeling (LM), competititve with transformers. We present a comprehensive theoretical study of the capacity of such SSMs as it compares to that of transformers and traditional RNNs.
  arXiv  Detail & Related papers  (2024-05-27T17:46:57Z)
• The Hidden Attention of Mamba Models [54.50526986788175]
  The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains. We show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers.
  arXiv  Detail & Related papers  (2024-03-03T18:58:21Z)
• Repeat After Me: Transformers are Better than State Space Models at Copying [53.47717661441142]
  We show that while generalized state space models are promising in terms of inference-time efficiency, they are limited compared to transformer models on tasks that require copying from the input context.
  arXiv  Detail & Related papers  (2024-02-01T21:44:11Z)
• Convolutional State Space Models for Long-Range Spatiotemporal Modeling [65.0993000439043]
  ConvS5 is an efficient variant for long-rangetemporal modeling. It significantly outperforms Transformers and ConvNISTTM on a long horizon Moving-Lab experiment while training 3X faster than ConvLSTM and generating samples 400X faster than Transformers.
  arXiv  Detail & Related papers  (2023-10-30T16:11:06Z)
• Non-autoregressive Streaming Transformer for Simultaneous Translation [45.96493039754171]
  Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality. We propose non-autoregressive streaming Transformer (NAST) NAST comprises a unidirectional encoder and a non-autoregressive decoder with intra-chunk parallelism.
  arXiv  Detail & Related papers  (2023-10-23T12:52:24Z)
• Block-State Transformers [41.57016890030355]
  State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies. We propose a hybrid layer named Block-State Transformer (BST) that internally combines an SSM sublayer for long-range contextualization. We show that our model outperforms similar Transformer-based architectures on language modeling perplexity and generalizes to longer sequences.
  arXiv  Detail & Related papers  (2023-06-15T22:48:08Z)
• Cascaded Text Generation with Markov Transformers [122.76100449018061]
  Two dominant approaches to neural text generation are fully autoregressive models, using serial beam search decoding, and non-autoregressive models, using parallel decoding with no output dependencies. This work proposes an autoregressive model with sub-linear parallel time generation. Noting that conditional random fields with bounded context can be decoded in parallel, we propose an efficient cascaded decoding approach for generating high-quality output. This approach requires only a small modification from standard autoregressive training, while showing competitive accuracy/speed tradeoff compared to existing methods on five machine translation datasets.
  arXiv  Detail & Related papers  (2020-06-01T17:52:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.