Sequence Layers

A neural network API and library (currently in Jax and TensorFlow 2) for easy creation of sequence models that can be executed both layer-by-layer (e.g. teacher forced training) and step-by-step (e.g. autoregressive sampling). It mitigates many common bugs arising in both streaming and parallel sequence processing around padding, resampling, and causality while giving a composable, declarative syntax.

You can read more about the design and features of SequenceLayers in our technical report, or quickly get started with our intro notebook:

Note: Only Jax support is installed by default. Use pip install sequence_layers[tensorflow] for TensorFlow.

Disclaimer: This is not an officially supported Google product.

Streamable networks, out of the box

A key feature of the library is that layers support streaming (step-by-step) operation. To achieve this, every layer has a notion of state when and a step function in addition to the typical layer-wise processing feature found in other libraries like Keras. When layers support a step method, their layer method produces identical results for the same sequence of input blocks enabling easy switching between step-wise and layer-wise processing depending on the use case.

Goals

Increased development velocity for both research and production applications of sequence modeling.

• Support for layer-by-layer and step-by-step processing in a single implementation.
• Declarative API.
• Composable, thin abstractions.
• Easy mix-and-match of popular sequence modeling paradigms (convolutional, recurrent, attention architectures).
• A quick path to deployment with tf.lite support for every layer.
• Tracking of invalid timesteps (those computed from padding).

Citation

If you found this library or its design concepts useful, we'd greatly appreciate a citation of our technical report:

@article{skerryryan2025sequencelayers,
  author       = {RJ Skerry-Ryan and Julian Salazar and Soroosh Mariooryad and David Kao and Daisy Stanton and Eric Battenberg and Matt Shannon and Ron J. Weiss and Robin Scheibler and Jonas Rothfuss and Tom Bagby},
  title        = {{S}equence{L}ayers: {S}equence Processing and Streaming Neural Networks Made Easy},
  journal      = {CoRR},
  volume       = {abs/2507.23292},
  year         = {2025}
}