def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# Call super() after tile creation, including ``reset_parameters``.
Linear.__init__(self, in_features, out_features, bias=bias)
# Create tiles
if rpu_config is None:
rpu_config = SingleRPUConfig()
AnalogModuleBase.__init__(
self,
in_features,
out_features,
bias,
realistic_read_write,
rpu_config.mapping
)
if self.analog_bias:
raise ModuleError("AnalogLinearMapped only supports digital bias.")
# More than one tile may need to be created. If so, divide
# weight matrix into equal pieces along input dimension with
# as many tiles as needed
max_input_size = rpu_config.mapping.max_input_size
max_output_size = rpu_config.mapping.max_output_size
self.in_sizes = self.get_split_sizes(in_features, max_input_size)
self.out_sizes = self.get_split_sizes(out_features, max_output_size)
self.analog_tile_array = []
for i, in_tile_size in enumerate(self.in_sizes):
in_tiles = []
for j, out_tile_size in enumerate(self.out_sizes):
tile = rpu_config.tile_class(out_tile_size,
in_tile_size,
rpu_config,
bias=self.analog_bias)
self.register_analog_tile(tile, name=f"{i}_{j}")
in_tiles.append(tile)
self.analog_tile_array.append(in_tiles)
# Set weights from the reset_parameters
self.set_weights(self.weight, self.bias, remap_weights=True,
weight_scaling_omega=weight_scaling_omega)
# Unregister weight/bias as a parameter but keep for sync
self.unregister_parameter('weight')
if self.analog_bias:
self.unregister_parameter('bias')
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Tuple[int, ...],
stride: Tuple[int, ...],
padding: Tuple[int, ...],
dilation: Tuple[int, ...],
transposed: bool,
output_padding: Tuple[int, ...],
groups: int,
bias: bool,
padding_mode: str,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# pylint: disable=too-many-arguments, too-many-locals
if groups != 1:
raise ValueError('Only one group is supported')
if padding_mode != 'zeros':
raise ValueError('Only "zeros" padding mode is supported')
# Call super() after tile creation, including ``reset_parameters``.
_ConvNd.__init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding, groups,
bias, padding_mode)
# Create the tile and set the analog.
if rpu_config is None:
rpu_config = SingleRPUConfig()
AnalogModuleBase.__init__(
self, self.get_tile_size(in_channels, groups, kernel_size),
out_channels, bias, realistic_read_write, rpu_config.mapping)
self.analog_tile = self._setup_tile(rpu_config)
# Register analog tile
self.register_analog_tile(self.analog_tile)
# Set weights from the reset_parameters
self.set_weights(self.weight,
self.bias,
remap_weights=True,
weight_scaling_omega=weight_scaling_omega)
# Set the index matrices.
self.fold_indices = Tensor().detach()
self.input_size = 0
self.tensor_view = (-1, ) # type: Tuple[int, ...]
# Unregister weight/bias as a parameter but keep it for syncs
self.unregister_parameter('weight')
if self.analog_bias:
self.unregister_parameter('bias')
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# Call super() after tile creation, including ``reset_parameters``.
Linear.__init__(self, in_features, out_features, bias=bias)
# Create tile
if rpu_config is None:
rpu_config = SingleRPUConfig()
AnalogModuleBase.__init__(
self,
in_features,
out_features,
bias,
realistic_read_write,
weight_scaling_omega,
rpu_config.mapping
)
self.analog_tile = self._setup_tile(rpu_config)
# Register tile
self.register_analog_tile(self.analog_tile)
# Set weights from the reset_parameters call
self.set_weights(self.weight, self.bias)
# Unregister weight/bias as a parameter but keep it as a
# field (needed for syncing still)
self.unregister_parameter('weight')
if self.analog_bias:
self.unregister_parameter('bias')
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Tuple[int, ...],
stride: Tuple[int, ...],
padding: Tuple[int, ...],
dilation: Tuple[int, ...],
transposed: bool,
output_padding: Tuple[int, ...],
groups: int,
bias: bool,
padding_mode: str,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# pylint: disable=too-many-arguments, too-many-locals
if groups != 1:
raise ValueError('Only one group is supported')
if padding_mode != 'zeros':
raise ValueError('Only "zeros" padding mode is supported')
# Call super() after tile creation, including ``reset_parameters``.
_ConvNd.__init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding, groups,
bias, padding_mode)
# Create tiles
if rpu_config is None:
rpu_config = SingleRPUConfig()
AnalogModuleBase.__init__(
self, self.get_tile_size(in_channels, groups,
kernel_size), out_channels, bias,
realistic_read_write, weight_scaling_omega, rpu_config.mapping)
if self.analog_bias:
raise ModuleError("AnalogConvNdMapped only supports digital bias.")
if not rpu_config:
rpu_config = SingleRPUConfig()
max_input_size = rpu_config.mapping.max_input_size
max_output_size = rpu_config.mapping.max_output_size
kernel_elem = self.in_features // self.in_channels
self.in_sizes = self.get_split_sizes(self.in_features, max_input_size,
kernel_elem)
self.out_sizes = self.get_split_sizes(self.out_features,
max_output_size)
self.analog_tile_array = []
for i, in_tile_size in enumerate(self.in_sizes):
in_tiles = []
for j, out_tile_size in enumerate(self.out_sizes):
tile = rpu_config.tile_class(out_tile_size,
in_tile_size * kernel_elem,
rpu_config,
bias=self.analog_bias)
self.register_analog_tile(tile, name=f"{i}_{j}")
in_tiles.append(tile)
self.analog_tile_array.append(in_tiles)
# Set weights from the reset_parameters (since now the
# analog_tiles are registered)
self.set_weights(self.weight, self.bias)
# Set the index matrices.
self.input_size = 0
self.fold_indices_lst = [] # type: List[Tensor]
# Unregister weight/bias as a parameter but keep it as a
# field (needed for syncing still)
self.unregister_parameter('weight')
if self.analog_bias:
self.unregister_parameter('bias')
