from copy import deepcopy
from typing import Optional, Tuple
from warnings import warn
import torch
import torch.nn as nn
import sinabs.layers as sl
DYNAPCNN_WEIGHT_PRECISION_BITS = 8
DYNAPCNN_STATE_PRECISION_BITS = 16
[docs]
def discretize_conv_spike(
conv_lyr: nn.Conv2d, spike_lyr: sl.IAF, to_int: bool = True
) -> Tuple[nn.Conv2d, sl.IAF]:
"""Discretize convolutional and spiking layers together.
This function takes a 2D convolutional and a spiking layer and returns a
copy of each, with discretized weights, bias and threshold.
Args:
conv_lyr (nn.Conv2d): Convolutional layer.
spike_lyr (sl.IAF): Spiking layer.
to_int (bool): Use integer types for discretized parameter.
Returns:
Tuple containing a discretized copy of convolutional layer and
a discretized copy of spiking layer.
"""
conv_lyr_copy = deepcopy(conv_lyr)
spike_lyr_copy = deepcopy(spike_lyr)
return discretize_conv_spike_(conv_lyr_copy, spike_lyr_copy, to_int=to_int)
[docs]
def discretize_conv_spike_(
conv_lyr: nn.Conv2d, spike_lyr: sl.IAF, to_int: bool = True
) -> Tuple[nn.Conv2d, sl.IAF]:
"""Discretize convolutional and spiking layers together, in-place.
This function takes a 2D convolutional and a spiking layer and discretizes
weights, bias and threshold in-place.
Args:
conv_lyr (nn.Conv2d): Convolutional layer.
spike_lyr (sl.IAF): Spiking layer.
to_int (bool): Use integer types for discretized parameter.
Returns:
A tuple containing a discretized convolutional layer and a
discretized spiking layer.
"""
return _discretize_conv_spk_(conv_lyr, spike_lyr, to_int=to_int)
[docs]
def discretize_conv(
layer: nn.Conv2d,
spk_thr: float,
spk_thr_low: float,
spk_state: Optional[torch.Tensor] = None,
to_int: bool = True,
) -> nn.Conv2d:
"""Discretize convolutional layer.
This function takes a 2D convolutional layer and parameters of a subsequent
spiking layer to return a discretized copy of the convolutional layer.
Args:
layer (nn.Conv2d): Convolutional layer.
spk_thr (float): Upper threshold of subsequent spiking layer.
spk_thr_low (float): Lower threshold of subsequent spiking layer.
spk_state (torch.Tensor): State of spiking layer. Defaults to None.
to_int (bool): Use integer types for discretized parameter. Defaults to True.
Returns:
Discretized copy of convolutional layer
"""
lyr_copy = deepcopy(layer)
layer_discr = discretize_conv_(
layer=lyr_copy,
spk_thr=spk_thr,
spk_thr_low=spk_thr_low,
spk_state=spk_state,
to_int=to_int,
)
return layer_discr
[docs]
def discretize_conv_(
layer: nn.Conv2d,
spk_thr: float,
spk_thr_low: float,
spk_state: Optional[torch.Tensor] = None,
to_int: bool = True,
) -> nn.Conv2d:
"""Discretize convolutional layer, in-place.
This function discretizes a 2D convolutional layer in-place, based on
parameters of a subsequent spiking layer.
Args:
layer (nn.Conv2d): Convolutional layer.
spk_thr (float): Upper threshold of subsequent spiking layer.
spk_thr_low (float): Lower threshold of subsequent spiking layer.
spk_state (torch.Tensor): State of spiking layer. Defaults to None.
to_int (bool): Use integer types for discretized parameter. Defaults to True.
Returns:
Discretized convolutional layer
"""
layer_discr, __ = _discretize_conv_spk_(
conv_lyr=layer,
spk_thr=spk_thr,
spk_thr_low=spk_thr_low,
spk_state=spk_state,
to_int=to_int,
)
return layer_discr
[docs]
def discretize_spk(
layer: sl.IAF,
conv_weight: torch.Tensor,
conv_bias: Optional[torch.Tensor] = None,
to_int: bool = True,
) -> sl.IAF:
"""Discretize spiking layer.
This function takes a spiking layer and parameters of a preceding
convolutional layer to return a discretized copy of the spiking layer.
Args:
layer (sl.IAF): Spiking layer.
conv_weight (torch.Tensor): Weight tensor of preceding convolutional layer.
conv_bias (torch.Tensor): Bias of preceding convolutional layer.
Optional argument, defaults to None.
to_int (bool): Use integer types for discretized parameter.
Returns:
Discretized copy of spiking layer
"""
lyr_copy = deepcopy(layer)
layer_discr = discretize_spk_(
layer=lyr_copy, conv_weight=conv_weight, conv_bias=conv_bias, to_int=to_int
)
return layer_discr
[docs]
def discretize_spk_(
layer: sl.IAF,
conv_weight: torch.Tensor,
conv_bias: Optional[torch.Tensor] = None,
to_int: bool = True,
) -> sl.IAF:
"""Discretize spiking layer in-place.
This function discretizes a spiking layer in-place, based on parameters of a
preceding convolutional layer.
Args:
layer (sl.IAF): Spiking layer.
conv_weight (torch.Tensor): Weight tensor of preceding convolutional layer.
conv_bias (torch.Tensor): Bias of preceding convolutional layer.
Optional argument, defaults to None.
to_int (bool): Use integer types for discretized parameter.
Returns:
Discretized spiking
"""
__, layer_discr = _discretize_conv_spk_(
spike_lyr=layer, conv_weight=conv_weight, conv_bias=conv_bias, to_int=to_int
)
return layer_discr
def _discretize_conv_spk_(
conv_lyr: Optional[nn.Conv2d] = None,
spike_lyr: Optional[sl.IAF] = None,
spk_thr: Optional[float] = None,
spk_thr_low: Optional[float] = None,
spk_state: Optional[torch.Tensor] = None,
conv_weight: Optional[torch.Tensor] = None,
conv_bias: Optional[torch.Tensor] = None,
to_int: bool = True,
) -> Tuple[nn.Conv2d, sl.IAF]:
"""Discretize convolutional and spiking layer.
Determine and apply a suitable scaling factor for weight and bias of
convolutional layer as well as thresholds and state of spiking layer, taking
into account current parameters and available precision on DYNAP-CNN. Instead of
providing layers, respective parameters can be provided directly. If a layer
is not provided, `None` will be returned instead of its discrete version.
Args:
conv_lyr (nn.Conv2d): Convolutional layer. Optional argument, defaults to None.
spike_lyr (sl.IAF): Spiking layer. Optional argument, defaults to None.
spk_thr (float): Upper threshold of spiking layer. Optional argument, defaults
to None. Has to be provided if `spike_lyr` is `None`.
Is ignored otherwise.
spk_thr_low (float): Lower threshold of spiking layer. Optional argument,
defaults to None. Has to be provided if `spike_lyr` is `None`.
Is ignored otherwise.
spk_state (torch.Tensor): State of spiking layer. Optional argument
defaults to None. Ignored if `spike_lyr` is not `None`.
conv_weight (torch.Tensor): Weight of convolutional layer. Optional argument,
defaults to None. Has to be provided if `conv_lyr` is `None`.
Is ignored otherwise.
conv_bias (torch.Tensor): Bias of convolutional layer. Optional argument,
defaults to None. Ignored if `conv_lyr` is not `None`.
to_int (bool): Use integer types for discretized parameters.
Returns:
Discretized convolutional layer if `conv_lyr` is not `None`, else `None`.
and discretized spiking layer if `spk_lyr` is not `None`, else `None`
"""
if conv_lyr is None:
discr_conv = False
if conv_weight is None:
raise TypeError("If `conv_lyr` is `None`, `weight` must be provided.")
if conv_bias is None:
conv_bias = torch.zeros(conv_weight.shape[0])
else:
if not isinstance(conv_lyr, nn.Conv2d):
raise TypeError("`conv_lyr` must be of type `Conv2d`")
discr_conv = True
# - Weights and bias
if conv_lyr.bias is not None:
conv_weight, conv_bias = conv_lyr.parameters()
else:
(conv_weight,) = conv_lyr.parameters()
conv_bias = torch.zeros(conv_lyr.out_channels)
if spike_lyr is None:
discr_spk = False
if spk_thr is None or spk_thr_low is None:
raise TypeError(
"If `spk_lyr` is `None`, both `spk_thr` and `spk_thr_low` must be provided."
)
# - Lower and upper thresholds in a tensor for easier handling
thresholds = torch.tensor((spk_thr_low, spk_thr))
else:
if not isinstance(spike_lyr, sl.IAF):
raise TypeError("`spike_lyr` must be of type `IAF`")
discr_spk = True
if spike_lyr.min_v_mem is None:
min_v_mem = -(2**15)
else:
min_v_mem = spike_lyr.min_v_mem
# - Lower and upper thresholds in a tensor for easier handling
thresholds = torch.tensor((min_v_mem, spike_lyr.spike_threshold))
# - Scaling of conv_weight, conv_bias, thresholds and neuron states
# Determine by which common factor conv_weight, conv_bias and thresholds can be scaled
# such each they matches its precision specificaitons.
scaling_w = determine_discretization_scale(
conv_weight, DYNAPCNN_WEIGHT_PRECISION_BITS
)
scaling_b = determine_discretization_scale(
conv_bias, DYNAPCNN_WEIGHT_PRECISION_BITS
)
scaling_t = determine_discretization_scale(
thresholds, DYNAPCNN_STATE_PRECISION_BITS
)
if spike_lyr is not None and spike_lyr.is_state_initialised():
scaling_n = determine_discretization_scale(
spike_lyr.v_mem, DYNAPCNN_STATE_PRECISION_BITS
)
scaling = min(scaling_w, scaling_b, scaling_t, scaling_n)
# Scale neuron state with common scaling factor and discretize
spike_lyr.v_mem.data = discretize_tensor(
spike_lyr.v_mem.data, scaling, to_int=to_int
).float()
else:
scaling = min(scaling_w, scaling_b, scaling_t)
# Scale conv_weight, conv_bias and thresholds with common scaling factor and discretize
if discr_conv:
conv_weight.data = discretize_tensor(
conv_weight, scaling, to_int=to_int
).float()
conv_bias.data = discretize_tensor(conv_bias, scaling, to_int=to_int).float()
if discr_spk:
min_v_mem, spike_threshold = discretize_tensor(
thresholds, scaling, to_int=to_int
)
spike_lyr.min_v_mem, spike_lyr.spike_threshold = nn.Parameter(
min_v_mem, requires_grad=False
), nn.Parameter(spike_threshold, requires_grad=False)
# Logic changes with use of activation functions
# TODO: Add check for the activation function in use
# if spike_lyr.membrane_subtract != spike_lyr.threshold:
# warn(
# "SpikingConv2dLayer: Subtraction of membrane potential is always by high threshold."
# )
return conv_lyr, spike_lyr
[docs]
def determine_discretization_scale(obj: torch.Tensor, bit_precision: int) -> float:
"""Determine a scale for discretization.
Determine how much the values of a torch tensor can be scaled in order to fit
the given precision
Args:
obj (torch.Tensor): Tensor that is to be scaled.
bit_precision (int): The precision in bits.
Returns:
The scaling factor
"""
# Discrete range
min_val_disc = -(2 ** (bit_precision - 1))
max_val_disc = 2 ** (bit_precision - 1) - 1
# Range in which values lie
min_val_obj = torch.min(obj)
max_val_obj = torch.max(obj)
# Determine if negative or positive values are to be considered for scaling
# Take into account that range for diescrete negative values is slightly larger than for positive
min_max_ratio_disc = abs(min_val_disc / max_val_disc)
if abs(min_val_obj) <= abs(max_val_obj) * min_max_ratio_disc:
scaling = abs(max_val_disc / max_val_obj)
else:
scaling = abs(min_val_disc / min_val_obj)
return scaling
[docs]
def discretize_tensor(
obj: torch.Tensor, scaling: float, to_int: bool = True
) -> torch.Tensor:
"""Scale a torch.Tensor and cast it to discrete integer values.
Args:
obj (torch.Tensor): Tensor that is to be discretized.
scaling (float): Scaling factor to be applied before discretization.
to_int (bool): If False, round the values, but don't cast to Int.
Defaults to True.
Returns:
Scaled and discretized copy of `obj`.
"""
# Scale the values
obj_scaled = obj * scaling
# Round and cast to integers
obj_scaled_rounded = torch.round(obj_scaled)
if to_int:
obj_scaled_rounded = obj_scaled_rounded.int()
return obj_scaled_rounded
[docs]
def discretize_scalar(obj: float, scaling: float) -> int:
"""Scale a float and cast it to discrete integer values.
Args:
obj (float): Value that is to be discretized.
scaling (float): Scaling factor to be applied before discretization.
Returns:
Scaled and discretized copy of `obj`.
"""
# Scale the values
obj_scaled = obj * float(scaling)
# Round and cast to integers
return round(obj_scaled)
