README.md

sysid-pytorch-lru

A PyTorch implementation of DeepMind's Linear Recurrent Unit (LRU). Application in System Identification included as example.

LRU block

The LRU block is a sequence-to-sequence model defined by a linear dynamical system and implemented in state-space form as:

$$\begin{align} x_{k} = Ax_{x-1} + B u_k\\\ y_k = \mathcal{R}[C x_k] + D u_k, \end{align}$$

where $A$ is diagonal and complex-valued; $B, C$ are full complex-valued; $D$ is full real-valued; and $\mathcal{R}[\cdot]$ denotes the real part of its argument.

Smart parameterization/initialization of the system matrices make the LRU block easy to train numerically. Moreover, the use of parallel scan algorithms makes execution extremely fast on modern hardware. For more details, read the paper!

Deep LRU Architecture

LRU units are typically organized in a deep LRU architecture like:

Basic usage:

The basic usage of the LRU block is illustrated in playground.ipynb:

import torch
from lru.linear import LRU

d_state = 200  # state dimension (x)
d_in = 100 # input dimension (u)
d_out = 10 # output dimension (y)
seq_len = 10000  # input sequence length
batch_size = 32

lru = LRU(
    in_features=d_in,
    out_features=d_out,
    state_features=d_state,
)

input_sequences = torch.randn((batch_size, seq_len, d_in))
x0 = torch.view_as_complex(
    torch.randn(batch_size, d_state, 2)
)

# slow loop implementation
output_sequences_loop = lru(input_sequences, mode="loop", state=x0)

# fast parallel scan implementation
output_sequences_scan = lru(input_sequences, mode="scan", state=x0)

Example

System identification of the Wiener-Hammerstein Benchmark, see files train.py and test.ipynb.