def plot_device(device: Union[PulsedDevice, UnitCell],
w_noise: float = 0.0,
**kwargs: Any) -> None:
"""Plot the step response figure for a given device (preset).
Note:
It will use an amount of read weight noise ``w_noise`` for
reading the weights.
Args:
device: PulsedDevice parameters
w_noise: Weight noise standard deviation during read
kwargs: for other parameters, see :func:`plot_response_overview`
"""
plt.figure(figsize=[7, 7])
# To simulate some weight read noise.
io_pars = IOParameters(
out_noise=0.0, # no out noise
w_noise=w_noise, # quite low
inp_res=-1., # turn off DAC
out_bound=100., # not limiting
out_res=-1., # turn off ADC
bound_management=BoundManagementType.NONE,
noise_management=NoiseManagementType.NONE,
w_noise_type=WeightNoiseType.ADDITIVE_CONSTANT)
if isinstance(device, PulsedDevice):
plot_response_overview(SingleRPUConfig(device=device, forward=io_pars),
**kwargs)
else:
plot_response_overview(
UnitCellRPUConfig(device=device, forward=io_pars), **kwargs)
def get_rpu_config(self):
return UnitCellRPUConfig(
device=TransferCompoundDevice(unit_cell_devices=[
SoftBoundsDevice(w_max_dtod=0, w_min_dtod=0),
SoftBoundsDevice(w_max_dtod=0, w_min_dtod=0)
],
transfer_forward=IOParameters(
is_perfect=True)))
def get_rpu_config(self):
return UnitCellRPUConfig(device=OneSidedUnitCell(
unit_cell_devices=[ConstantStepDevice(w_max_dtod=0, w_min_dtod=0)
]))
def get_rpu_config(self):
return UnitCellRPUConfig(device=ReferenceUnitCell(unit_cell_devices=[
SoftBoundsDevice(w_max_dtod=0, w_min_dtod=0),
SoftBoundsDevice(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]])
# The Tiki-taka learning rule can be implemented using the transfer device.
rpu_config = UnitCellRPUConfig(
device=TransferCompound(
# Devices that compose the Tiki-taka compound.
unit_cell_devices=[
SoftBoundsDevice(w_min=-0.3, w_max=0.3),
SoftBoundsDevice(w_min=-0.6, w_max=0.6)
],
# Make some adjustments of the way Tiki-Taka is performed.
units_in_mbatch=True, # batch_size=1 anyway
transfer_every=2, # every 2 batches do a transfer-read
n_cols_per_transfer=1, # one forward read for each transfer
gamma=0.0, # all SGD weight in second device
scale_transfer_lr=True, # in relative terms to SGD LR
transfer_lr=1.0, # same transfer LR as for SGD
)
)
# Make more adjustments (can be made here or above).
rpu_config.forward.inp_res = 1/64. # 6 bit DAC
# same backward pass settings as forward
rpu_config.backward = rpu_config.forward
def get_rpu_config(device: Union[PulsedDevice, UnitCell], io_pars: IOParameters) \
-> Union[SingleRPUConfig, UnitCellRPUConfig]:
if isinstance(device, PulsedDevice):
return SingleRPUConfig(device=device, forward=io_pars)
return UnitCellRPUConfig(device=device, forward=io_pars)
def get_rpu_config(self):
return UnitCellRPUConfig(device=DifferenceUnitCellDevice(
unit_cell_devices=[ConstantStepDevice(w_max_dtod=0, w_min_dtod=0)
]))
from aihwkit.simulator.configs import UnitCellRPUConfig
from aihwkit.simulator.configs.utils import VectorUnitCellUpdatePolicy
from aihwkit.simulator.configs.devices import (
ConstantStepDevice,
VectorUnitCell
)
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 single unit cell devices (of the same type) per cross-point.
rpu_config.device = VectorUnitCell(
unit_cell_devices=[
ConstantStepDevice(w_max=0.3),
ConstantStepDevice(w_max_dtod=0.4),
ConstantStepDevice(up_down_dtod=0.1),
])
# 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)
from aihwkit.simulator.configs.devices import (
ConstantStepDevice,
VectorUnitCell,
LinearStepDevice,
SoftBoundsDevice,
ReferenceUnitCell)
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
from aihwkit.optim.analog_sgd import AnalogSGD
from aihwkit.simulator.configs import UnitCellRPUConfig
from aihwkit.simulator.configs.devices import (ConstantStepDevice,
VectorUnitCellDevice,
LinearStepDevice,
SoftBoundsDevice)
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 = VectorUnitCellDevice(unit_cell_devices=[
ConstantStepDevice(),
LinearStepDevice(w_max_dtod=0.4),
SoftBoundsDevice()
])
# Only one of the devices should receive a single update.
rpu_config.device.single_device_update = True
# That is selected randomly, the effective weights is the sum of all
# weights.
rpu_config.device.single_device_update_random = True
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
