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 custom_device(**kwargs):
"""Custom device """
return SoftBoundsDevice(w_max_dtod=0.0,
w_min_dtod=0.0,
w_max=1.0,
w_min=-1.0,
**kwargs)
def get_rpu_config(self):
return DigitalRankUpdateRPUConfig(device=MixedPrecisionCompound(
device=SoftBoundsDevice(w_max_dtod=0, w_min_dtod=0),
transfer_every=1), )
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)
]))
def get_rpu_config(self):
return SingleRPUConfig(
device=SoftBoundsDevice(w_max_dtod=0, w_min_dtod=0))
from aihwkit.simulator.configs.devices import (
TransferCompound,
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]])
# 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
from aihwkit.simulator.configs.devices import (MixedPrecisionCompound,
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]])
# Select the device model to use in the training. While one can use a
# presets as well, we here build up the RPU config from more basic
# devices. We use the relevant RPU config for using a digital rank
# update and transfer to analog device (like in mixed precision) and
# set it to a mixed precision compound which in turn uses a
# ConstantStep analog device:
rpu_config = DigitalRankUpdateRPUConfig(
device=MixedPrecisionCompound(device=SoftBoundsDevice(), ))
# print the config (default values are omitted)
print('\nPretty-print of non-default settings:\n')
print(rpu_config)
print('\nInfo about all settings:\n')
print(repr(rpu_config))
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
# a more detailed printout of the instantiated
print('\nInfo about the instantiated C++ tile:\n')
print(model.analog_tile.tile)
# Move the model and tensors to cuda if it is available.
from aihwkit.simulator.configs.devices import (MixedPrecisionCompound,
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]])
# Select the device model to use in the training. While one can use a
# presets as well, we here build up the RPU config from more basic
# devices. We use the relevant RPU config for using a digital rank
# update and transfer to analog device (like in mixed precision) and
# set it to a mixed precision compound which in turn uses a
# ConstantStep analog device:
rpu_config = DigitalRankUpdateRPUConfig(device=MixedPrecisionCompound(
device=SoftBoundsDevice(),
# adjust quantization level (0 means FP)
n_x_bins=5, # quantization bins of the digital rank update (activation)
n_d_bins=3 # quantization bins of the digital rank update (error)
))
# print the config (default values are omitted)
print('\nPretty-print of non-default settings:\n')
print(rpu_config)
print('\nInfo about all settings:\n')
print(repr(rpu_config))
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
# a more detailed printout of the instantiated
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
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
print(rpu_config)
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)
print(model.analog_tile.tile)
# Move the model and tensors to cuda if it is available.
if cuda.is_compiled():
x = x.cuda()
