pytorch2ltspice

pytorch2ltspice converts PyTorch nn.Sequential models into LTspice-compatible subcircuits (.subckt). It is intended for workflows where a controller is trained in PyTorch and then embedded directly into LTspice simulations for power electronics, control, and reinforcement learning experiments.

By combining it with LTspicePowerSim, you can evaluate neural-network controllers inside converter, inverter, and motor-drive simulations.

Features

• Export PyTorch nn.Sequential models as LTspice .subckt netlists.
• Support nn.Linear, nn.ReLU, nn.Sigmoid, nn.Tanh, nn.RNNCell, nn.GRUCell, and nn.LSTMCell.
• Generate behavioral-source (B) based netlists for feed-forward layers.
• Generate recurrent-cell implementations using LTspice .machine blocks with an auto-added CLK pin.
• Support final-output postprocessing via output_activation and selective output exposure via output_mask.
• Include helper utilities under pytorch2ltspice.utils for signal generation, model scaffolding, and sampling.
• Provide end-to-end example projects for buck-converter control with imitation learning and PPO.

Installation

Install from PyPI:

pip install pytorch2ltspice

Install from source:

git clone https://github.com/kosokno/pytorch2ltspice.git
cd pytorch2ltspice
pip install -e .

Quick Start

Define a model in PyTorch:

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(20, 32),
    nn.ReLU(),
    nn.Linear(32, 16),
    nn.ReLU(),
    nn.Linear(16, 1),
)
model.eval()

Export it as an LTspice subcircuit:

from pytorch2ltspice import export_model_to_ltspice

export_model_to_ltspice(
    model,
    filename="TEST_MODEL_SUBCKT.SP",
    subckt_name="TESTACTORSUBCKT",
    output_activation=["tanh"],
)

Include the generated file in LTspice:

• Add .include TEST_MODEL_SUBCKT.SP to your schematic.
• Drive the NNIN* pins from your circuit.
• Read the inference result from NNOUT*.

Utilities

Helper utilities are available under pytorch2ltspice.utils:

• siggen: generate LTspice signal-source schematics and symbols.
• modelgen: generate a PyTorch module class from nn.Sequential.
• sampling: sample signals on a clock for analysis and data preparation.

Examples

Example projects are available under examples/:

• BUCK_VM_BI: behavior-imitation controller for a voltage-mode buck converter.
• BUCK_VM_PPO: PPO-based controller for a voltage-mode buck converter.
• BUCK_VM_GRU8x1: recurrent controller examples using GRUCell.

Related Projects

• LTspicePowerSim

License

MIT