diff --git a/modneflib/modnef/modnef_torch/model.py b/modneflib/modnef/modnef_torch/model.py
index 44443dc4da4759f9c0c785f4df1e2b3c9493cca2..ae40072c5329af8bd93e3af75f5d0bc358364a00 100644
--- a/modneflib/modnef/modnef_torch/model.py
+++ b/modneflib/modnef/modnef_torch/model.py
@@ -88,7 +88,7 @@ class ModNEFModel(nn.Module):
for m in self.modules():
if isinstance(m, ModNEFNeuron):
m.hardware_estimation(hardware)
- m.set_quant(quant)
+ m.run_quantize(quant)
return super().train(mode=mode)
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/blif.py b/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/blif.py
index 83cc04fcb4300966ea80fba91575c66944fa52df..50f18d89dd311cfcba5fffe6ead72cdabfe1fa3f 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/blif.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/blif.py
@@ -18,7 +18,7 @@ from modnef.arch_builder.modules.utilities import *
from ..modnef_torch_neuron import ModNEFNeuron, _quantizer
from modnef.quantizer import *
-class BLIF(Leaky, ModNEFNeuron):
+class BLIF(ModNEFNeuron):
"""
ModNEFTorch BLIF neuron model
@@ -111,26 +111,15 @@ class BLIF(Leaky, ModNEFNeuron):
quantization method
"""
- Leaky.__init__(
- self=self,
- beta=beta,
- threshold=threshold,
- spike_grad=spike_grad,
- surrogate_disable=False,
- init_hidden=False,
- inhibition=False,
- learn_beta=False,
- learn_threshold=False,
- reset_mechanism=reset_mechanism,
- state_quant=False,
- output=False,
- graded_spikes_factor=1.0,
- learn_graded_spikes_factor=False,
- )
-
- ModNEFNeuron.__init__(self=self, quantizer=quantizer)
-
- self.fc = nn.Linear(in_features, out_features, bias=False)
+ super().__init__(threshold=threshold,
+ in_features=in_features,
+ out_features=out_features,
+ reset_mechanism=reset_mechanism,
+ spike_grad=spike_grad,
+ quantizer=quantizer
+ )
+
+ self.register_buffer("beta", torch.tensor(beta))
self._init_mem()
@@ -307,50 +296,20 @@ class BLIF(Leaky, ModNEFNeuron):
output_path=output_path
)
return module
-
- def quantize_weight(self):
- """
- Quantize synaptic weight
- """
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight)
-
- self.fc.weight.data = self.quantizer(self.fc.weight.data, True)
-
- def quantize_parameters(self):
- """
- Quantize neuron hyper-parameters
- """
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight)
-
- self.threshold.data = self.quantizer(self.threshold.data, True)
- self.beta.data = self.quantizer(self.beta.data, True)
-
- def quantize(self, force_init=False):
+ def quantize_hp(self, unscale : bool = True):
"""
- Quantize synaptic weight and neuron hyper-parameters
+ neuron hyper-parameters quantization.
+ We assume you already initialize quantizer
Parameters
----------
- force_init = Fasle : bool
- force quantizer initialization
- """
-
- if force_init or not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight)
-
- self.quantize_weight()
- self.quantize_parameters()
- self.quantization_flag = True
-
- def clamp(self):
- """
- Clamp synaptic weight and neuron hyper-parameters
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
- self.fc.weight.data = self.quantizer.clamp(self.fc.weight.data)
+ self.threshold.data = self.quantizer(self.threshold.data, unscale)
+ self.beta.data = self.quantizer(self.beta.data, unscale)
@classmethod
def detach_hidden(cls):
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/rblif.py b/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/rblif.py
index bf702cb48e12f6e598d3d0c12c0af77ac01fa183..29bd28444b1fd195c469d1b70132cbbc1a8a9b63 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/rblif.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/blif_model/rblif.py
@@ -18,7 +18,7 @@ from ..modnef_torch_neuron import ModNEFNeuron, _quantizer
from math import log, ceil
from modnef.quantizer import *
-class RBLIF(Leaky, ModNEFNeuron):
+class RBLIF(ModNEFNeuron):
"""
ModNEFTorch reccurent BLIF neuron model
@@ -116,27 +116,17 @@ class RBLIF(Leaky, ModNEFNeuron):
quantization method
"""
- Leaky.__init__(
- self=self,
- beta=beta,
- threshold=threshold,
- spike_grad=spike_grad,
- surrogate_disable=False,
- init_hidden=False,
- inhibition=False,
- learn_beta=False,
- learn_threshold=False,
- reset_mechanism=reset_mechanism,
- state_quant=False,
- output=False,
- graded_spikes_factor=1.0,
- learn_graded_spikes_factor=False,
- )
-
- ModNEFNeuron.__init__(self=self, quantizer=quantizer)
+ super().__init__(threshold=threshold,
+ in_features=in_features,
+ out_features=out_features,
+ reset_mechanism=reset_mechanism,
+ spike_grad=spike_grad,
+ quantizer=quantizer
+ )
+
+ self.register_buffer("beta", torch.tensor(beta))
- self.fc = nn.Linear(in_features, out_features, bias=False)
- self.reccurent = nn.Linear(out_features, out_features, bias=False)
+ self.reccurent = nn.Linear(out_features, out_features, bias=True)
self._init_mem()
@@ -321,52 +311,19 @@ class RBLIF(Leaky, ModNEFNeuron):
)
return module
- def quantize_weight(self):
- """
- Quantize synaptic weight
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight, self.reccurent.weight)
-
- self.fc.weight.data = self.quantizer(self.fc.weight.data, True)
- self.reccurent.weight.data = self.quantizer(self.reccurent.weight.data, True)
-
- def quantize_parameters(self):
- """
- Quantize neuron hyper-parameters
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight, self.reccurent.weight)
-
- self.threshold.data = self.quantizer(self.threshold.data, True)
- self.beta.data = self.quantizer(self.beta.data, True)
-
- def quantize(self, force_init=False):
+ def quantize_hp(self, unscale : bool = True):
"""
- Quantize synaptic weight and neuron hyper-parameters
+ neuron hyper-parameters quantization.
+ We assume you already initialize quantizer
Parameters
----------
- force_init = Fasle : bool
- force quantizer initialization
- """
-
- if force_init or not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight, self.reccurent.weight)
-
- self.quantize_weight()
- self.quantize_parameters()
- self.quantization_flag = True
-
- def clamp(self):
- """
- Clamp synaptic weight and neuron hyper-parameters
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
- self.fc.weight.data = self.quantizer.clamp(self.fc.weight.data)
- self.reccurent.weight.data = self.quantizer.clamp(self.reccurent.weight.data)
+ self.threshold.data = self.quantizer(self.threshold.data, unscale)
+ self.beta.data = self.quantizer(self.beta.data, unscale)
@classmethod
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/modnef_torch_neuron.py b/modneflib/modnef/modnef_torch/modnef_neurons/modnef_torch_neuron.py
index 0a1acd91e0af892d9b43ca10623d5d6203af65dc..08e37abea078bacf98ae9e6ef2dd2c53e3bd3c6a 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/modnef_torch_neuron.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/modnef_torch_neuron.py
@@ -9,6 +9,7 @@ Descriptions: ModNEF torch neuron interface builder
"""
import torch
+import torch.nn as nn
from modnef.quantizer import *
from snntorch._neurons import SpikingNeuron
from snntorch.surrogate import fast_sigmoid
@@ -44,26 +45,22 @@ class ModNEFNeuron(SpikingNeuron):
create and return the corresponding modnef archbuilder module from internal neuron parameters
"""
- def __init__(self,
- threshold,
- reset_mechanism,
- quantizer : Quantizer,
- spike_grad=fast_sigmoid(slope=25)):
-
- SpikingNeuron.__init__(
- self=self,
+ def __init__(self,
+ in_features,
+ out_features,
+ threshold,
+ reset_mechanism,
+ spike_grad,
+ quantizer):
+
+ super().__init__(
threshold=threshold,
- reset_mechanism=reset_mechanism,
- spike_gard=spike_grad,
- surrogate_disable=False,
- init_hidden=False,
- inhibition=False,
- learn_threshold=False,
- state_quant=False,
- output=False,
- graded_spikes_factor=1.0,
- learn_graded_spikes_factor=False
+ spike_grad=spike_grad,
+ reset_mechanism=reset_mechanism
)
+
+ self.fc = nn.Linear(in_features=in_features, out_features=out_features, bias=False)
+
self.hardware_estimation_flag = False
self.quantization_flag = False
@@ -84,19 +81,20 @@ class ModNEFNeuron(SpikingNeuron):
raise NotImplementedError()
def init_quantizer(self):
+ """
+ Initialize internal or re-initialize internal quantizer
+ """
- params = list(self.parameters())
-
- w1 = params[0].data
+ param = list(self.parameters())
- if len(params)==2:
- w2 = params[0].data
+ if len(param)==1:
+ self.quantizer.init_from_weight(param[0])
+ print("init no rec")
else:
- w2 = torch.zeros((1))
-
- self.quantizer.init_quantizer(w1, w2)
+ self.quantizer.init_from_weight(param[0], param[1])
+ print("init rec")
- def quantize_weight(self, unscaled : bool = False):
+ def quantize_weight(self, unscale : bool = True):
"""
synaptic weight quantization
@@ -105,12 +103,20 @@ class ModNEFNeuron(SpikingNeuron):
NotImplementedError()
"""
- for param in self.parameters():
- param.data = self.quantizer(param.data, unscale=unscaled)
+ for p in self.parameters():
+ p.data = self.quantizer(p.data, unscale=unscale)
+ print(p)
+ print("quantize weight")
- def quantize_hp(self):
+ def quantize_hp(self, unscale : bool = True):
"""
neuron hyper-parameters quantization
+ We assume you've already intialize quantizer
+
+ Parameters
+ ----------
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
raise NotImplementedError()
@@ -125,16 +131,31 @@ class ModNEFNeuron(SpikingNeuron):
force quantizer initialization
"""
- raise NotImplementedError()
+ if force_init:
+ self.init_quantizer()
+
+ self.quantize_weight()
+ self.quantize_hp()
def clamp(self):
"""
Clamp synaptic weight
"""
- raise NotImplementedError()
+ for p in self.parameters():
+ p.data = self.quantizer.clamp(p.data)
+ print("clamp")
- def set_quant(self, mode=False):
+ def run_quantize(self, mode=False):
+ """
+ Srtup quantization flag
+
+ Parameters
+ ----------
+ mode : bool = False
+ quantize run or not
+ """
+
self.quantization_flag = mode
def hardware_estimation(self, mode = False):
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/rslif.py b/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/rslif.py
index 2d78aa9440df32af8fa63f9e11218a89d5588483..190ddc84029476ef7c36e144c4fc9c57d3c21403 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/rslif.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/rslif.py
@@ -18,7 +18,7 @@ from ..modnef_torch_neuron import ModNEFNeuron, _quantizer
from math import ceil, log
from modnef.quantizer import MinMaxQuantizer
-class RSLIF(LIF, ModNEFNeuron):
+class RSLIF(ModNEFNeuron):
"""
ModNEFTorch reccurent Simplifed LIF neuron model
@@ -119,33 +119,18 @@ class RSLIF(LIF, ModNEFNeuron):
quantization function
"""
- LIF.__init__(
- self=self,
- beta = v_leak,
- threshold=threshold,
- spike_grad=spike_grad,
- surrogate_disable=False,
- init_hidden=False,
- inhibition=False,
- learn_beta=False,
- learn_threshold=False,
- reset_mechanism="zero",
- state_quant=False,
- output=False,
- graded_spikes_factor=1.0,
- learn_graded_spikes_factor=False
- )
-
- ModNEFNeuron.__init__(self, quantizer=quantizer)
+ super().__init__(threshold=threshold,
+ in_features=in_features,
+ out_features=out_features,
+ reset_mechanism="zero",
+ spike_grad=spike_grad,
+ quantizer=quantizer
+ )
self.register_buffer("v_leak", torch.as_tensor(v_leak))
self.register_buffer("v_min", torch.as_tensor(v_min))
self.register_buffer("v_rest", torch.as_tensor(v_rest))
- self.in_features = in_features
- self.out_features = out_features
-
- self.fc = nn.Linear(self.in_features, self.out_features, bias=False)
self.reccurent = nn.Linear(self.out_features, self.out_features, bias=False)
self._init_mem()
@@ -338,56 +323,23 @@ class RSLIF(LIF, ModNEFNeuron):
output_path=output_path
)
return module
-
- def quantize_weight(self):
- """
- Quantize synaptic weight
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight, rec_weight=self.reccurent.weight)
- self.fc.weight.data = self.quantizer(self.fc.weight.data, True)
- self.reccurent.weight.data = self.quantizer(self.reccurent.weight.data, True)
-
- def quantize_parameters(self):
- """
- Quantize neuron hyper-parameters
+ def quantize_hp(self, unscale : bool = True):
"""
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight, rec_weight=self.reccurent.weight)
-
- self.v_leak.data = self.quantizer(self.v_leak.data, True)
- self.v_min.data = self.quantizer(self.v_min.data, True)
- self.v_rest.data = self.quantizer(self.v_rest.data, True)
- self.threshold.data = self.quantizer(self.threshold, True)
-
- def quantize(self, force_init=False):
- """
- Quantize synaptic weight and neuron hyper-parameters
+ neuron hyper-parameters quantization
+ We assume you've already intialize quantizer
Parameters
----------
- force_init = Fasle : bool
- force quantizer initialization
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
-
- if force_init or not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight, self.reccurent.weight)
- self.quantize_weight()
- self.quantize_parameters()
+ self.v_leak.data = self.quantizer(self.v_leak.data, unscale)
+ self.v_min.data = self.quantizer(self.v_min.data, unscale)
+ self.v_rest.data = self.quantizer(self.v_rest.data, unscale)
+ self.threshold.data = self.quantizer(self.threshold, unscale)
- def clamp(self):
- """
- Clamp synaptic weight and neuron hyper-parameters
- """
-
- self.fc.weight.data = self.quantizer.clamp(self.fc.weight.data)
- self.reccurent.weight.data = self.quantizer.clamp(self.reccurent.weight.data)
-
-
@classmethod
def detach_hidden(cls):
"""Returns the hidden states, detached from the current graph.
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/slif.py b/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/slif.py
index 9d01858ccd432208fe1924dd629f53b881b0ba31..78cc36b27622e701758b8f10b18ecb17634dd84c 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/slif.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/slif_model/slif.py
@@ -18,7 +18,7 @@ from ..modnef_torch_neuron import ModNEFNeuron, _quantizer
from math import ceil, log
from modnef.quantizer import MinMaxQuantizer
-class SLIF(LIF, ModNEFNeuron):
+class SLIF(ModNEFNeuron):
"""
ModNEFTorch reccurent Simplifed LIF neuron model
@@ -119,34 +119,18 @@ class SLIF(LIF, ModNEFNeuron):
quantization method
"""
- LIF.__init__(
- self=self,
- beta = v_leak,
- threshold=threshold,
- spike_grad=spike_grad,
- surrogate_disable=False,
- init_hidden=False,
- inhibition=False,
- learn_beta=False,
- learn_threshold=False,
- reset_mechanism="zero",
- state_quant=False,
- output=False,
- graded_spikes_factor=1.0,
- learn_graded_spikes_factor=False
- )
-
- ModNEFNeuron.__init__(self, quantizer=quantizer)
+ super().__init__(threshold=threshold,
+ in_features=in_features,
+ out_features=out_features,
+ reset_mechanism="zero",
+ spike_grad=spike_grad,
+ quantizer=quantizer
+ )
self.register_buffer("v_leak", torch.as_tensor(v_leak))
self.register_buffer("v_min", torch.as_tensor(v_min))
self.register_buffer("v_rest", torch.as_tensor(v_rest))
- self.in_features = in_features
- self.out_features = out_features
-
- self.fc = nn.Linear(self.in_features, self.out_features, bias=False)
-
self._init_mem()
self.hardware_description = {
@@ -329,53 +313,23 @@ class SLIF(LIF, ModNEFNeuron):
output_path=output_path
)
return module
-
- def quantize_weight(self):
- """
- Quantize synaptic weight
- """
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight)
-
- self.fc.weight.data = self.quantizer(self.fc.weight.data, True)
-
- def quantize_parameters(self):
+ def quantize_hp(self, unscale : bool = True):
"""
- Quantize neuron hyper-parameters
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight)
-
- self.v_leak.data = self.quantizer(self.v_leak.data, True)
- self.v_min.data = self.quantizer(self.v_min.data, True)
- self.v_rest.data = self.quantizer(self.v_rest.data, True)
- self.threshold.data = self.quantizer(self.threshold, True)
-
- def quantize(self, force_init=False):
- """
- Quantize synaptic weight and neuron hyper-parameters
+ neuron hyper-parameters quantization
+ We assume you've already intialize quantizer
Parameters
----------
- force_init = Fasle : bool
- force quantizer initialization
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
-
- if force_init or not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight)
- self.quantize_weight()
- self.quantize_parameters()
+ self.v_leak.data = self.quantizer(self.v_leak.data, unscale)
+ self.v_min.data = self.quantizer(self.v_min.data, unscale)
+ self.v_rest.data = self.quantizer(self.v_rest.data, unscale)
+ self.threshold.data = self.quantizer(self.threshold, unscale)
- def clamp(self):
- """
- Clamp synaptic weight and neuron hyper-parameters
- """
-
- self.fc.weight.data = self.quantizer.clamp(self.fc.weight.data)
-
@classmethod
def detach_hidden(cls):
"""Returns the hidden states, detached from the current graph.
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/rshiftlif.py b/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/rshiftlif.py
index cdf4d2678e00a525c30a4ffdc106699ffd7ac344..e6354a2086b3b742ffbf6270df64e31e8f46c8a7 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/rshiftlif.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/rshiftlif.py
@@ -19,7 +19,7 @@ from math import log, ceil
from modnef.quantizer import DynamicScaleFactorQuantizer
-class RShiftLIF(LIF, ModNEFNeuron):
+class RShiftLIF(ModNEFNeuron):
"""
ModNEFTorch reccurent Shift LIF neuron model
@@ -116,35 +116,25 @@ class RShiftLIF(LIF, ModNEFNeuron):
quantizer = DynamicScaleFactoirQuantizer(8) : Quantizer
quantization method
"""
+
+ super().__init__(threshold=threshold,
+ in_features=in_features,
+ out_features=out_features,
+ reset_mechanism=reset_mechanism,
+ spike_grad=spike_grad,
+ quantizer=quantizer
+ )
self.shift = int(-log(1-beta)/log(2))
if (1-2**-self.shift) != beta:
print(f"initial value of beta ({beta}) has been change for {1-2**-self.shift} = 1-2**-{self.shift}")
beta = 1-2**-self.shift
-
- LIF.__init__(
- self=self,
- beta=beta,
- threshold=threshold,
- spike_grad=spike_grad,
- surrogate_disable=False,
- init_hidden=False,
- inhibition=False,
- learn_beta=False,
- learn_threshold=False,
- reset_mechanism=reset_mechanism,
- state_quant=False,
- output=False,
- graded_spikes_factor=1.0,
- learn_graded_spikes_factor=False,
- )
-
- ModNEFNeuron.__init__(self=self, quantizer=quantizer)
- self.fc = nn.Linear(in_features, out_features, bias=False)
self.reccurent = nn.Linear(out_features, out_features, bias=False)
+ self.register_buffer("beta", torch.tensor(beta))
+
self._init_mem()
self.hardware_description = {
@@ -340,51 +330,19 @@ class RShiftLIF(LIF, ModNEFNeuron):
)
return module
- def quantize_weight(self):
- """
- Quantize synaptic weight
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight, rec_weight=self.reccurent.weight)
-
- self.fc.weight.data = self.quantizer(self.fc.weight.data, True)
- self.reccurent.weight.data = self.quantizer(self.reccurent.weight.data, True)
-
- def quantize_parameters(self):
+ def quantize_hp(self, unscale : bool = True):
"""
- Quantize neuron hyper-parameters
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight, rec_weight=self.reccurent.weight)
-
- self.threshold.data = self.quantizer(self.threshold.data, True)
- self.beta.data = self.quantizer(self.beta.data, True)
-
- def quantize(self, force_init=False):
- """
- Quantize synaptic weight and neuron hyper-parameters
+ neuron hyper-parameters quantization.
+ We assume you already initialize quantizer
Parameters
----------
- force_init = Fasle : bool
- force quantizer initialization
- """
-
- if force_init or not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight, self.reccurent.weight)
-
- self.quantize_weight()
- self.quantize_parameters()
-
- def clamp(self):
- """
- Clamp synaptic weight and neuron hyper-parameters
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
- self.fc.weight.data = self.quantizer.clamp(self.fc.weight.data)
- self.reccurent.weight.data = self.quantizer.clamp(self.reccurent.weight.data)
+ self.threshold.data = self.quantizer(self.threshold.data, unscale)
+ self.beta.data = self.quantizer(self.beta.data, unscale)
@classmethod
diff --git a/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/shiftlif.py b/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/shiftlif.py
index 195e965f8f99ef994bca289886b182ad7af402fa..f2c95cbe6d5d7ef0ad7271e134a9a1d600f0e2c5 100644
--- a/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/shiftlif.py
+++ b/modneflib/modnef/modnef_torch/modnef_neurons/srlif_model/shiftlif.py
@@ -17,6 +17,7 @@ from modnef.arch_builder.modules.utilities import *
from ..modnef_torch_neuron import ModNEFNeuron, _quantizer
from math import log, ceil
from modnef.quantizer import DynamicScaleFactorQuantizer
+from snntorch import LIF
class ShiftLIF(ModNEFNeuron):
"""
@@ -110,6 +111,15 @@ class ShiftLIF(ModNEFNeuron):
quantizer = DynamicScaleFactorQuantizer(8) : Quantizer
quantization method
"""
+
+ super().__init__(threshold=threshold,
+ in_features=in_features,
+ out_features=out_features,
+ reset_mechanism=reset_mechanism,
+ spike_grad=spike_grad,
+ quantizer=quantizer
+ )
+
self.shift = int(-log(1-beta)/log(2))
@@ -118,14 +128,7 @@ class ShiftLIF(ModNEFNeuron):
beta = 1-2**-self.shift
- ModNEFNeuron.__init__(self=self,
- threshold=threshold,
- reset_mechanism=reset_mechanism,
- spike_grad=spike_grad,
- quantizer=quantizer
- )
-
- self.fc = nn.Linear(in_features, out_features, bias=False)
+ self.register_buffer("beta", torch.tensor(beta))
self._init_mem()
@@ -310,48 +313,19 @@ class ShiftLIF(ModNEFNeuron):
)
return module
- def quantize_weight(self):
- """
- Quantize synaptic weight
- """
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight)
-
- self.fc.weight.data = self.quantizer(self.fc.weight.data, True)
-
- def quantize_parameters(self):
- """
- Quantize neuron hyper-parameters
+ def quantize_hp(self, unscale : bool = True):
"""
-
- if not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(weight=self.fc.weight)
-
- self.threshold.data = self.quantizer(self.threshold.data, True)
-
- def quantize(self, force_init=False):
- """
- Quantize synaptic weight and neuron hyper-parameters
+ neuron hyper-parameters quantization.
+ We assume you already initialize quantizer
Parameters
----------
- force_init = Fasle : bool
- force quantizer initialization
- """
-
- if force_init or not self.quantizer.is_initialize:
- self.quantizer.init_from_weight(self.fc.weight)
-
- self.quantize_weight()
- self.quantize_parameters()
-
- def clamp(self):
- """
- Clamp synaptic weight and neuron hyper-parameters
+ unscale : bool = True
+ set to true if quantization must be simulate
"""
- self.fc.weight.data = self.quantizer.clamp(self.fc.weight.data)
+ self.threshold.data = self.quantizer(self.threshold.data, unscale)
+ self.beta.data = self.quantizer(self.beta.data, unscale)
@classmethod
diff --git a/modneflib/modnef/quantizer/dynamic_scale_quantizer.py b/modneflib/modnef/quantizer/dynamic_scale_quantizer.py
index 43a6aa1fca47cb9e36775b0596a95a0b9c61c6b9..2631170b678e8ece885ed61e4e16a2efe17340db 100644
--- a/modneflib/modnef/quantizer/dynamic_scale_quantizer.py
+++ b/modneflib/modnef/quantizer/dynamic_scale_quantizer.py
@@ -96,7 +96,7 @@ class DynamicScaleFactorQuantizer(Quantizer):
is_initialize=config["is_initialize"]
)
- def init_quantizer(self, weight, rec_weight=torch.zeros((1))):
+ def init_from_weight(self, weight, rec_weight=torch.zeros((1))):
"""
initialize quantizer parameters from synaptic weight
diff --git a/modneflib/modnef/quantizer/fixed_point_quantizer.py b/modneflib/modnef/quantizer/fixed_point_quantizer.py
index c48e690cdbc31e131a1351c02330c7a8fc3e148c..106a242180b8b83b5d47ebe1dbf0e4f7604f63cd 100644
--- a/modneflib/modnef/quantizer/fixed_point_quantizer.py
+++ b/modneflib/modnef/quantizer/fixed_point_quantizer.py
@@ -109,7 +109,7 @@ class FixedPointQuantizer(Quantizer):
is_initialize=config["is_initialize"]
)
- def init_quantizer(self, weight, rec_weight=torch.zeros((1))):
+ def init_from_weight(self, weight, rec_weight=torch.zeros((1))):
"""
initialize quantizer parameters from synaptic weight
diff --git a/modneflib/modnef/quantizer/min_max_quantizer.py b/modneflib/modnef/quantizer/min_max_quantizer.py
index ebc4ae9fb6cde07e030b35dad1a89f0c82f6ae00..6ca813180dfc9d0de66ff4428564c6240b9f4649 100644
--- a/modneflib/modnef/quantizer/min_max_quantizer.py
+++ b/modneflib/modnef/quantizer/min_max_quantizer.py
@@ -105,7 +105,7 @@ class MinMaxQuantizer(Quantizer):
is_initialize=config["is_initialize"]
)
- def init_quantizer(self, weight, rec_weight=torch.zeros((1))):
+ def init_from_weight(self, weight, rec_weight=torch.zeros((1))):
"""
initialize quantizer parameters from synaptic weight
diff --git a/modneflib/modnef/quantizer/quantizer.py b/modneflib/modnef/quantizer/quantizer.py
index 6ab752eb28bb5c601293cfe46cd1ce7e05ff4e5a..c56f50616be06d383a33840424ae199967f35403 100644
--- a/modneflib/modnef/quantizer/quantizer.py
+++ b/modneflib/modnef/quantizer/quantizer.py
@@ -77,7 +77,7 @@ class Quantizer():
raise NotImplementedError()
- def init_quantizer(self, weight, rec_weight=torch.zeros((1))):
+ def init_from_weight(self, weight, rec_weight=torch.zeros((1))):
"""
initialize quantizer parameters from synaptic weight
@@ -108,14 +108,14 @@ class Quantizer():
"""
if not torch.is_tensor(data):
- tdata = torch.tensor(data)
+ tdata = torch.tensor(data, dtype=torch.float32)
else:
tdata = data
qdata = self._quant(tdata)
if unscale:
- qdata = self._unquant(qdata)
+ qdata = self._unquant(qdata).to(torch.float32)
if isinstance(data, (int, float)):
return qdata.item()