01_simple_layer.py

# -*- coding: utf-8 -*-

# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.

"""aihwkit example 1: simple network with one layer.

Simple network that consist of one analog layer. The network aims to learn
to sum all the elements from one array.
"""
# pylint: disable=invalid-name

# Imports from PyTorch.
from torch import Tensor
from torch.nn.functional import mse_loss

# Imports from aihwkit.
from aihwkit.nn import AnalogLinear
from aihwkit.optim import AnalogSGD
from aihwkit.simulator.configs import SingleRPUConfig, ConstantStepDevice
from aihwkit.simulator.rpu_base import cuda

# Prepare the datasets (input and expected output).
x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
y = Tensor([[1.0, 0.5], [0.7, 0.3]])

# Define a single-layer network, using a constant step device type.
rpu_config = SingleRPUConfig(device=ConstantStepDevice())

model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)

# Move the model and tensors to cuda if it is available.
if cuda.is_compiled():
    x = x.cuda()
    y = y.cuda()
    model = model.cuda()

# Define an analog-aware optimizer, preparing it for using the layers.
opt = AnalogSGD(model.parameters(), lr=0.1)
opt.regroup_param_groups(model)

for epoch in range(100):
    # Delete old gradient
    opt.zero_grad()
    # Add the training Tensor to the model (input).
    pred = model(x)
    # Add the expected output Tensor.
    loss = mse_loss(pred, y)
    # Run training (backward propagation).
    loss.backward()

    opt.step()

    print("Loss error: {:.16f}".format(loss))