def test_hidden_parameter_mismatch(self):
"""Test for error if tile structure mismatches."""
model = self.get_layer()
state_dict = model.state_dict()
# Create the device and the array.
rpu_config = SingleRPUConfig(
device=LinearStepDevice() # different hidden structure
)
new_model = self.get_layer(rpu_config=rpu_config)
if new_model.analog_tile.__class__.__name__ != model.analog_tile.__class__.__name__:
with self.assertRaises(TileError):
self.assertRaises(new_model.load_state_dict(state_dict))
def get_rpu_config(self):
return SingleRPUConfig(
device=LinearStepDevice(w_max_dtod=0, w_min_dtod=0))
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 vector device having multiple
# devices per crosspoint. Each device can be arbitrarily defined
rpu_config = UnitCellRPUConfig()
# 3 arbitrary devices per cross-point.
rpu_config.device = VectorUnitCell(
unit_cell_devices=[
ReferenceUnitCell(unit_cell_devices=[SoftBoundsDevice(w_max=1.0)]),
ConstantStepDevice(),
LinearStepDevice(w_max_dtod=0.4),
SoftBoundsDevice()
])
# Only one of the devices should receive a single update.
# That is selected randomly, the effective weights is the sum of all
# weights.
rpu_config.device.update_policy = VectorUnitCellUpdatePolicy.SINGLE_RANDOM
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
print(rpu_config)
# Move the model and tensors to cuda if it is available.
if cuda.is_compiled():