Hippox provides a simple dataclass for initializing High-order Polynomial Projection Operators (HiPPOs) as parameters in JAX neural network libraries such as Flax and Haiku.
Here is an example of initializing HiPPO parameters inside a Haiku module:
import haiku as hk
from hippox.main import Hippo
class MyHippoModule(hk.Module):
def __init__(self, state_size, measure)
_hippo = Hippo(state_size=state_size, measure=measure)
_hippo()
self._lambda_real = hk.get_parameter(
'lambda_real',
shape=[state_size,],
init = _hippo.lambda_initializer('real')
)
self._lambda_imag = hk.get_parameter(
'lambda_imaginary',
shape=[state_size,],
init = _hippo.lambda_initializer('imaginary')
)
self._state_matrix = self._lambda_real + 1j * self._lambda_imag
self._input_matrix = hk.get_parameter(
'input_matrix',
shape=[state_size, 1],
init=_hippo.b_initializer()
)
def __call__(input, prev_state):
new_state = self._state_matrix @ prev_state + self._input_matrix @ input
return new_stateIf using a library (such as Equinox) which does not require an initializer function but simply takes JAX ndarrays for parameterization, then you can call the HiPPO matrices directly as a property of the base class after it has been called:
import equinox as eqx
from hippox.main import Hippo
class MyHippoModule(eqx.Module):
A: jnp.ndarray
B: jnp.ndarray
def __init__(self, state_size, measure)
_hippo = Hippo(state_size=state_size, measure=measure)
_hippo_params = _hippo()
self.A = _hippo_params.state_matrix
self.B = _hippo_params.input_matrix
def __call__(input, prev_state):
new_state = self.A @ prev_state + self.B @ input
return new_statehippox can be easily installed through PyPi:
pip install hippox
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https://github.com/HazyResearch/state-spaces - Original paper implementations in PyTorch
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https://github.com/srush/annotated-s4 - JAX implementation of S4 models (S4, S4D, DSS)
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HiPPO: Recurrent Memory with Optimal Polynomial Projections: https://arxiv.org/abs/2008.07669 - Original paper which introduced HiPPOs
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Efficiently Modeling Long Sequences with Structured State Spaces: https://arxiv.org/abs/2111.00396 - S4 paper, introduces normal/diagonal plus low rank decomposition
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How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections: https://arxiv.org/abs/2206.12037 - Generalizes and explains the core principals behind HiPPO
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On the Parameterization and Initialization of Diagonal State Space Models: https://arxiv.org/abs/2206.11893 - S4D paper, details and explains the diagonal only parameterization
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