README.md

Long Range Models

A collection of simple implementations of long-range sequence models, including LRU, S5, and S4. More implementations to come.

Install

$ pip install long-range-models

Usage

This library offers detailed documentation for every module and layer implemented. Models are created by composing different pieces together. Check out the examples below.

Discrete sequence data

Consider a language model built with an LRU sequence layer and the architecture proposed in the S4 paper:

from functools import partial
import jax.random as jrandom
from long_range_models import SequenceModel, S4Module, LRULayer

rng = jrandom.PRNGKey(0)

model = SequenceModel(
  num_tokens=1000,
  module=S4Module(
    sequence_layer=partial(LRULayer, state_dim=256),
    dim=128,
    depth=6,
  ),
)

x = jrandom.randint(rng, (1, 1024), 0, 1000)

variables = model.init(rng, x)
model.apply(variables, x)  # (1, 1024, 1000)

Continuous sequence data

For sequences with continuous values, the setup looks as follows:

from functools import partial
import jax.random as jrandom
from long_range_models import ContinuousSequenceModel, S4Module, LRULayer

rng = jrandom.PRNGKey(0)

model = ContinuousSequenceModel(
  out_dim=10,
  module=S4Module(
    sequence_layer=partial(LRULayer, state_dim=256),
    dim=128,
    depth=6,
  ),
)

x = jrandom.normal(rng, (1, 1024, 32))

variables = model.init(rng, x)
model.apply(variables, x)  # (1, 1024, 10)

Note: both model types offer several customization options. Make sure to check out their documentation.

Upcoming features

• More implementations: Extend the library with models like S4D, S4Liquid, BiGS, Hyena, RetNet, SGConv, H3, and others.
• Customization: Allow users to better customize currently implemented layers and architectures (e.g., activation functions, initialization, etc.).
• Sequential API: Allow recurrent models to run sequentially, allowing for efficient inference.