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from typing import Callable, overload
import torch
import torch.nn as nn
from spikingjelly.clock_driven import surrogate, base, lava_exchange
from spikingjelly import configure
import math
import numpy as np
import logging
import cupy
from spikingjelly.clock_driven import neuron_kernel, cu_kernel_opt
try:
import lava.lib.dl.slayer as slayer
except BaseException as e:
logging.info(f'spikingjelly.clock_driven.neuron: {e}')
slayer = None
def check_backend(backend: str):
if backend == 'torch':
return
elif backend == 'cupy':
assert cupy is not None, 'CuPy is not installed! You can install it from "https://github.com/cupy/cupy".'
elif backend == 'lava':
assert slayer is not None, 'Lava-DL is not installed! You can install it from "https://github.com/lava-nc/lava-dl".'
else:
raise NotImplementedError(backend)
class BaseNode(base.MemoryModule):
def __init__(self, v_threshold: float = 1., v_reset: float = 0.,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False):
"""
* :ref:`API in English <BaseNode.__init__-en>`
.. _BaseNode.__init__-cn:
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
可微分SNN神经元的基类神经元。
* :ref:`中文API <BaseNode.__init__-cn>`
.. _BaseNode.__init__-en:
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
This class is the base class of differentiable spiking neurons.
"""
assert isinstance(v_reset, float) or v_reset is None
assert isinstance(v_threshold, float)
assert isinstance(detach_reset, bool)
super().__init__()
if v_reset is None:
self.register_memory('v', 0.)
else:
self.register_memory('v', v_reset)
self.register_memory('v_threshold', v_threshold)
self.register_memory('v_reset', v_reset)
self.detach_reset = detach_reset
self.surrogate_function = surrogate_function
@abstractmethod
def neuronal_charge(self, x: torch.Tensor):
"""
* :ref:`API in English <BaseNode.neuronal_charge-en>`
.. _BaseNode.neuronal_charge-cn:
定义神经元的充电差分方程。子类必须实现这个函数。
* :ref:`中文API <BaseNode.neuronal_charge-cn>`
.. _BaseNode.neuronal_charge-en:
Define the charge difference equation. The sub-class must implement this function.
"""
raise NotImplementedError
def neuronal_fire(self):
"""
* :ref:`API in English <BaseNode.neuronal_fire-en>`
.. _BaseNode.neuronal_fire-cn:
根据当前神经元的电压、阈值，计算输出脉冲。
* :ref:`中文API <BaseNode.neuronal_fire-cn>`
.. _BaseNode.neuronal_fire-en:
Calculate out spikes of neurons by their current membrane potential and threshold voltage.
"""
return self.surrogate_function(self.v - self.v_threshold)
def neuronal_reset(self, spike):
"""
* :ref:`API in English <BaseNode.neuronal_reset-en>`
.. _BaseNode.neuronal_reset-cn:
根据当前神经元释放的脉冲，对膜电位进行重置。
* :ref:`中文API <BaseNode.neuronal_reset-cn>`
.. _BaseNode.neuronal_reset-en:
Reset the membrane potential according to neurons' output spikes.
"""
if self.detach_reset:
spike_d = spike.detach()
else:
spike_d = spike
if self.v_reset is None:
# soft reset
self.v = self.v - spike_d * self.v_threshold
else:
# hard reset
self.v = (1. - spike_d) * self.v + spike_d * self.v_reset
def extra_repr(self):
return f'v_threshold={self.v_threshold}, v_reset={self.v_reset}, detach_reset={self.detach_reset}'
def forward(self, x: torch.Tensor):
"""
* :ref:`API in English <BaseNode.forward-en>`
.. _BaseNode.forward-cn:
:param x: 输入到神经元的电压增量
:type x: torch.Tensor
:return: 神经元的输出脉冲
:rtype: torch.Tensor
按照充电、放电、重置的顺序进行前向传播。
* :ref:`中文API <BaseNode.forward-cn>`
.. _BaseNode.forward-en:
:param x: increment of voltage inputted to neurons
:type x: torch.Tensor
:return: out spikes of neurons
:rtype: torch.Tensor
Forward by the order of `neuronal_charge`, `neuronal_fire`, and `neuronal_reset`.
"""
self.neuronal_charge(x)
spike = self.neuronal_fire()
self.neuronal_reset(spike)
return spike
class AdaptiveBaseNode(BaseNode):
def __init__(self, v_threshold: float = 1., v_reset: float = 0.,
v_rest: float = 0., w_rest: float = 0, tau_w: float = 2., a: float = 0., b: float = 0.,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False):
# b: jump amplitudes
# a: subthreshold coupling
assert isinstance(w_rest, float)
assert isinstance(v_rest, float)
assert isinstance(tau_w, float)
assert isinstance(a, float)
assert isinstance(b, float)
super.__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.register_memory('w', w_rest)
self.w_rest = w_rest
self.v_rest = v_rest
self.tau_w = tau_w
self.a = a
self.b = b
def neuronal_adaptation(self, spike):
self.w = self.w + 1. / self.tau_w * (self.a * (self.v - self.v_rest) - self.w) + self.b * spike
def extra_repr(self):
return super().extra_repr() + f', v_rest={self.v_rest}, w_rest={self.w_rest}, tau_w={self.tau_w}, a={self.a}, b={self.b}'
@overload
def forward(self, x: torch.Tensor):
self.neuronal_charge(x)
spike = self.neuronal_fire()
self.neuronal_adaptation(spike)
self.neuronal_reset(spike)
return spike
class IFNode(BaseNode):
def __init__(self, v_threshold: float = 1., v_reset: float = 0.,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False,
cupy_fp32_inference=False):
"""
* :ref:`API in English <IFNode.__init__-en>`
.. _IFNode.__init__-cn:
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
:param cupy_fp32_inference: 若为 `True`，在 `eval` 模式下，使用float32，却在GPU上运行，并且 `cupy` 已经安装，则会自动使用 `cupy` 进行加速
:type cupy_fp32_inference: bool
Integrate-and-Fire 神经元模型，可以看作理想积分器，无输入时电压保持恒定，不会像LIF神经元那样衰减。其阈下神经动力学方程为：
.. math::
V[t] = V[t-1] + X[t]
* :ref:`中文API <IFNode.__init__-cn>`
.. _IFNode.__init__-en:
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
:param cupy_fp32_inference: If `True`, if this module is in `eval` mode, using float32, running on GPU, and `cupy` is installed, then this
module will use `cupy` to accelerate
:type cupy_fp32_inference: bool
The Integrate-and-Fire neuron, which can be seen as a ideal integrator. The voltage of the IF neuron will not decay
as that of the LIF neuron. The subthreshold neural dynamics of it is as followed:
.. math::
V[t] = V[t-1] + X[t]
"""
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
if cupy_fp32_inference:
check_backend('cupy')
self.cupy_fp32_inference = cupy_fp32_inference
def neuronal_charge(self, x: torch.Tensor):
self.v = self.v + x
def forward(self, x: torch.Tensor):
if self.cupy_fp32_inference and cupy is not None and not self.training and x.dtype == torch.float32:
# cupy is installed && eval mode && fp32
device_id = x.get_device()
if device_id < 0:
return super().forward(x)
# use cupy to accelerate
if isinstance(self.v, float):
v = torch.zeros_like(x)
if self.v != 0.:
torch.fill_(v, self.v)
self.v = v
if self.v_reset is None:
hard_reset = False
else:
hard_reset = True
code = rf'''
extern "C" __global__
void IFNode_{'hard' if hard_reset else 'soft'}_reset_inference_forward(
const float * x, const float & v_threshold, {'const float & v_reset,' if hard_reset else ''}
float * spike, float * v,
const int & numel)
'''
code += r'''
{
const int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < numel)
{
v[index] += x[index];
spike[index] = (float) (v[index] >= v_threshold);
'''
code += rf'''
{'v[index] = (1.0f - spike[index]) * v[index] + spike[index] * v_reset;' if hard_reset else 'v[index] -= spike[index] * v_threshold;'}
'''
code += r'''
}
}
'''
if hasattr(self, 'cp_kernel'):
if self.cp_kernel.code != code:
# replace codes
del self.cp_kernel
self.cp_kernel = cupy.RawKernel(code,
f"IFNode_{'hard' if hard_reset else 'soft'}_reset_inference_forward",
options=configure.cuda_compiler_options,
backend=configure.cuda_compiler_backend)
else:
self.cp_kernel = cupy.RawKernel(code,
f"IFNode_{'hard' if hard_reset else 'soft'}_reset_inference_forward",
options=configure.cuda_compiler_options,
backend=configure.cuda_compiler_backend)
with cu_kernel_opt.DeviceEnvironment(device_id):
numel = x.numel()
threads = configure.cuda_threads
blocks = cu_kernel_opt.cal_blocks(numel)
cp_numel = cupy.asarray(numel)
cp_v_threshold = cupy.asarray(self.v_threshold, dtype=np.float32)
if hard_reset:
cp_v_reset = cupy.asarray(self.v_reset, dtype=np.float32)
spike = torch.zeros_like(x)
if hard_reset:
x, cp_v_threshold, cp_v_reset, spike, self.v, cp_numel = cu_kernel_opt.get_contiguous(x,
cp_v_threshold,
cp_v_reset,
spike, self.v,
cp_numel)
kernel_args = [x, cp_v_threshold, cp_v_reset, spike, self.v, cp_numel]
else:
x, cp_v_threshold, spike, self.v, cp_numel = cu_kernel_opt.get_contiguous(x, cp_v_threshold, spike,
self.v, cp_numel)
kernel_args = [x, cp_v_threshold, spike, self.v, cp_numel]
self.cp_kernel(
(blocks,), (threads,),
cu_kernel_opt.wrap_args_to_raw_kernel(
device_id,
*kernel_args
)
)
return spike
else:
return super().forward(x)
class MultiStepIFNode(IFNode):
def __init__(self, v_threshold: float = 1., v_reset: float = 0.,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False, backend='torch',
lava_s_cale=1 << 6):
"""
* :ref:`API in English <MultiStepIFNode.__init__-en>`
.. _MultiStepIFNode.__init__-cn:
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
:param backend: 使用哪种计算后端，可以为 ``'torch'`` 或 ``'cupy'``。``'cupy'`` 速度更快，但仅支持GPU。
:type backend: str
多步版本的 :class:`spikingjelly.clock_driven.neuron.IFNode`。
.. tip::
对于多步神经元，输入 ``x_seq.shape = [T, *]``，不仅可以使用 ``.v`` 和 ``.spike`` 获取 ``t = T - 1`` 时刻的电压和脉冲，还能够
使用 ``.v_seq`` 和 ``.spike_seq`` 获取完整的 ``T`` 个时刻的电压和脉冲。
.. tip::
阅读 :doc:`传播模式 <./clock_driven/10_propagation_pattern>` 以获取更多关于单步和多步传播的信息。
* :ref:`中文API <MultiStepIFNode.__init__-cn>`
.. _MultiStepIFNode.__init__-en:
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
:param backend: use which backend, ``'torch'`` or ``'cupy'``. ``'cupy'`` is faster but only supports GPU
:type backend: str
The multi-step version of :class:`spikingjelly.clock_driven.neuron.IFNode`.
.. admonition:: Tip
:class: tip
The input for multi-step neurons are ``x_seq.shape = [T, *]``. We can get membrane potential and spike at
time-step ``t = T - 1`` by ``.v`` and ``.spike``. We can also get membrane potential and spike at all ``T``
time-steps by ``.v_seq`` and ``.spike_seq``.
.. admonition:: Tip
:class: tip
Read :doc:`Propagation Pattern <./clock_driven_en/10_propagation_pattern>` for more details about single-step
and multi-step propagation.
"""
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.register_memory('v_seq', None)
check_backend(backend)
self.backend = backend
self.lava_s_cale = lava_s_cale
if backend == 'lava':
self.lava_neuron = self.to_lava()
else:
self.lava_neuron = None
def forward(self, x_seq: torch.Tensor):
assert x_seq.dim() > 1
# x_seq.shape = [T, *]
if self.backend == 'torch':
spike_seq = []
self.v_seq = []
for t in range(x_seq.shape[0]):
spike_seq.append(super().forward(x_seq[t]).unsqueeze(0))
self.v_seq.append(self.v.unsqueeze(0))
spike_seq = torch.cat(spike_seq, 0)
self.v_seq = torch.cat(self.v_seq, 0)
return spike_seq
elif self.backend == 'cupy':
if isinstance(self.v, float):
v_init = self.v
self.v = torch.zeros_like(x_seq[0].data)
if v_init != 0.:
torch.fill_(self.v, v_init)
spike_seq, self.v_seq = neuron_kernel.MultiStepIFNodePTT.apply(
x_seq.flatten(1), self.v.flatten(0), self.v_threshold, self.v_reset, self.detach_reset,
self.surrogate_function.cuda_code)
spike_seq = spike_seq.reshape(x_seq.shape)
self.v_seq = self.v_seq.reshape(x_seq.shape)
self.v = self.v_seq[-1].clone()
return spike_seq
elif self.backend == 'lava':
if self.lava_neuron is None:
self.lava_neuron = self.to_lava()
spike, self.v = lava_exchange.lava_neuron_forward(self.lava_neuron, x_seq, self.v)
return spike
else:
raise NotImplementedError(self.backend)
def extra_repr(self):
return super().extra_repr() + f', backend={self.backend}'
def to_lava(self):
return lava_exchange.to_lava_neuron(self)
def reset(self):
super().reset()
if self.lava_neuron is not None:
self.lava_neuron.current_state.zero_()
self.lava_neuron.voltage_state.zero_()
class LIFNode(BaseNode):
def __init__(self, tau: float = 2., decay_input: bool = True, v_threshold: float = 1.,
v_reset: float = 0., surrogate_function: Callable = surrogate.Sigmoid(),
detach_reset: bool = False, cupy_fp32_inference=False):
"""
* :ref:`API in English <LIFNode.__init__-en>`
.. _LIFNode.__init__-cn:
:param tau: 膜电位时间常数
:type tau: float
:param decay_input: 输入是否会衰减
:type decay_input: bool
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
:param cupy_fp32_inference: 若为 `True`，在 `eval` 模式下，使用float32，却在GPU上运行，并且 `cupy` 已经安装，则会自动使用 `cupy` 进行加速
:type cupy_fp32_inference: bool
Leaky Integrate-and-Fire 神经元模型，可以看作是带漏电的积分器。其阈下神经动力学方程为：
若 ``decay_input == True``:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] - (V[t-1] - V_{reset}))
若 ``decay_input == False``:
.. math::
V[t] = V[t-1] - \\frac{1}{\\tau}(V[t-1] - V_{reset}) + X[t]
.. tip::
在 `eval` 模式下，使用float32，却在GPU上运行，并且 `cupy` 已经安装，则会自动使用 `cupy` 进行加速。
* :ref:`中文API <LIFNode.__init__-cn>`
.. _LIFNode.__init__-en:
:param tau: membrane time constant
:type tau: float
:param decay_input: whether the input will decay
:type decay_input: bool
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
:param cupy_fp32_inference: If `True`, if this module is in `eval` mode, using float32, running on GPU, and `cupy` is installed, then this
module will use `cupy` to accelerate
:type cupy_fp32_inference: bool
The Leaky Integrate-and-Fire neuron, which can be seen as a leaky integrator.
The subthreshold neural dynamics of it is as followed:
IF ``decay_input == True``:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] - (V[t-1] - V_{reset}))
IF ``decay_input == False``:
.. math::
V[t] = V[t-1] - \\frac{1}{\\tau}(V[t-1] - V_{reset}) + X[t]
.. admonition:: Tip
:class: tip
If this module is in `eval` mode, using float32, running on GPU, and `cupy` is installed, then this
module will use `cupy` to accelerate.
"""
assert isinstance(tau, float) and tau > 1.
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.tau = tau
self.decay_input = decay_input
if cupy_fp32_inference:
check_backend('cupy')
self.cupy_fp32_inference = cupy_fp32_inference
def extra_repr(self):
return super().extra_repr() + f', tau={self.tau}'
def neuronal_charge(self, x: torch.Tensor):
if self.decay_input:
if self.v_reset is None or self.v_reset == 0.:
self.v = self.v + (x - self.v) / self.tau
else:
self.v = self.v + (x - (self.v - self.v_reset)) / self.tau
else:
if self.v_reset is None or self.v_reset == 0.:
self.v = self.v * (1. - 1. / self.tau) + x
else:
self.v = self.v - (self.v - self.v_reset) / self.tau + x
def forward(self, x: torch.Tensor):
if self.cupy_fp32_inference and cupy is not None and not self.training and x.dtype == torch.float32:
# cupy is installed && eval mode && fp32
device_id = x.get_device()
if device_id < 0:
return super().forward(x)
# use cupy to accelerate
if isinstance(self.v, float):
v = torch.zeros_like(x)
if self.v != 0.:
torch.fill_(v, self.v)
self.v = v
if self.v_reset is None:
hard_reset = False
else:
hard_reset = True
code = rf'''
extern "C" __global__
void LIFNode_{'hard' if hard_reset else 'soft'}_reset_decayInput_{self.decay_input}_inference_forward(
const float * x, const float & v_threshold, {'const float & v_reset,' if hard_reset else ''} const float & tau,
float * spike, float * v,
const int & numel)
'''
code += r'''
{
const int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < numel)
{
'''
if self.decay_input:
if hard_reset:
code += r'''
v[index] += (x[index] - (v[index] - v_reset)) / tau;
'''
else:
code += r'''
v[index] += (x[index] - v[index]) / tau;
'''
else:
if hard_reset:
code += r'''
v[index] = x[index] + v[index] - (v[index] - v_reset) / tau;
'''
else:
code += r'''
v[index] = x[index] + v[index] * (1.0f - 1.0f / tau);
'''
code += rf'''
spike[index] = (float) (v[index] >= v_threshold);
{'v[index] = (1.0f - spike[index]) * v[index] + spike[index] * v_reset;' if hard_reset else 'v[index] -= spike[index] * v_threshold;'}
'''
code += r'''
}
}
'''
if hasattr(self, 'cp_kernel'):
if self.cp_kernel.code != code:
# replace codes
del self.cp_kernel
self.cp_kernel = cupy.RawKernel(code,
f"LIFNode_{'hard' if hard_reset else 'soft'}_reset_decayInput_{self.decay_input}_inference_forward",
options=configure.cuda_compiler_options,
backend=configure.cuda_compiler_backend)
else:
self.cp_kernel = cupy.RawKernel(code,
f"LIFNode_{'hard' if hard_reset else 'soft'}_reset_decayInput_{self.decay_input}_inference_forward",
options=configure.cuda_compiler_options,
backend=configure.cuda_compiler_backend)
with cu_kernel_opt.DeviceEnvironment(device_id):
numel = x.numel()
threads = configure.cuda_threads
blocks = cu_kernel_opt.cal_blocks(numel)
cp_numel = cupy.asarray(numel)
cp_v_threshold = cupy.asarray(self.v_threshold, dtype=np.float32)
if hard_reset:
cp_v_reset = cupy.asarray(self.v_reset, dtype=np.float32)
cp_tau = cupy.asarray(self.tau, dtype=np.float32)
spike = torch.zeros_like(x)
if hard_reset:
x, cp_v_threshold, cp_v_reset, cp_tau, spike, self.v, cp_numel = cu_kernel_opt.get_contiguous(x,
cp_v_threshold,
cp_v_reset,
cp_tau,
spike,
self.v,
cp_numel)
kernel_args = [x, cp_v_threshold, cp_v_reset, cp_tau, spike, self.v, cp_numel]
else:
x, cp_v_threshold, cp_tau, spike, self.v, cp_numel = cu_kernel_opt.get_contiguous(x, cp_v_threshold,
cp_tau, spike,
self.v, cp_numel)
kernel_args = [x, cp_v_threshold, cp_tau, spike, self.v, cp_numel]
self.cp_kernel(
(blocks,), (threads,),
cu_kernel_opt.wrap_args_to_raw_kernel(
device_id,
*kernel_args
)
)
return spike
else:
return super().forward(x)
class MultiStepLIFNode(LIFNode):
def __init__(self, tau: float = 2., decay_input: bool = True, v_threshold: float = 1.,
v_reset: float = 0., surrogate_function: Callable = surrogate.Sigmoid(),
detach_reset: bool = False, backend='torch', lava_s_cale=1 << 6):
"""
* :ref:`API in English <MultiStepLIFNode.__init__-en>`
.. _MultiStepLIFNode.__init__-cn:
:param tau: 膜电位时间常数
:type tau: float
:param decay_input: 输入是否会衰减
:type decay_input: bool
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
:param backend: 使用哪种计算后端，可以为 ``'torch'`` 或 ``'cupy'``。``'cupy'`` 速度更快，但仅支持GPU。
:type backend: str
多步版本的 :class:`spikingjelly.clock_driven.neuron.LIFNode`。
.. tip::
对于多步神经元，输入 ``x_seq.shape = [T, *]``，不仅可以使用 ``.v`` 和 ``.spike`` 获取 ``t = T - 1`` 时刻的电压和脉冲，还能够
使用 ``.v_seq`` 和 ``.spike_seq`` 获取完整的 ``T`` 个时刻的电压和脉冲。
.. tip::
阅读 :doc:`传播模式 <./clock_driven/10_propagation_pattern>` 以获取更多关于单步和多步传播的信息。
* :ref:`中文API <MultiStepLIFNode.__init__-cn>`
.. _MultiStepLIFNode.__init__-en:
:param tau: membrane time constant
:type tau: float
:param decay_input: whether the input will decay
:type decay_input: bool
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
:param backend: use which backend, ``'torch'`` or ``'cupy'``. ``'cupy'`` is faster but only supports GPU
:type backend: str
The multi-step version of :class:`spikingjelly.clock_driven.neuron.LIFNode`.
.. admonition:: Tip
:class: tip
The input for multi-step neurons are ``x_seq.shape = [T, *]``. We can get membrane potential and spike at
time-step ``t = T - 1`` by ``.v`` and ``.spike``. We can also get membrane potential and spike at all ``T``
time-steps by ``.v_seq`` and ``.spike_seq``.
.. admonition:: Tip
:class: tip
Read :doc:`Propagation Pattern <./clock_driven_en/10_propagation_pattern>` for more details about single-step
and multi-step propagation.
"""
super().__init__(tau, decay_input, v_threshold, v_reset, surrogate_function, detach_reset)
self.register_memory('v_seq', None)
check_backend(backend)
self.backend = backend
self.lava_s_cale = lava_s_cale
if backend == 'lava':
self.lava_neuron = self.to_lava()
else:
self.lava_neuron = None
def forward(self, x_seq: torch.Tensor):
assert x_seq.dim() > 1
# x_seq.shape = [T, *]
if self.backend == 'torch':
spike_seq = []
self.v_seq = []
for t in range(x_seq.shape[0]):
spike_seq.append(super().forward(x_seq[t]).unsqueeze(0))
self.v_seq.append(self.v.unsqueeze(0))
spike_seq = torch.cat(spike_seq, 0)
self.v_seq = torch.cat(self.v_seq, 0)
return spike_seq
elif self.backend == 'cupy':
if isinstance(self.v, float):
v_init = self.v
self.v = torch.zeros_like(x_seq[0].data)
if v_init != 0.:
torch.fill_(self.v, v_init)
spike_seq, self.v_seq = neuron_kernel.MultiStepLIFNodePTT.apply(
x_seq.flatten(1), self.v.flatten(0), self.decay_input, self.tau, self.v_threshold, self.v_reset,
self.detach_reset, self.surrogate_function.cuda_code)
spike_seq = spike_seq.reshape(x_seq.shape)
self.v_seq = self.v_seq.reshape(x_seq.shape)
self.v = self.v_seq[-1].clone()
return spike_seq
elif self.backend == 'lava':
if self.lava_neuron is None:
self.lava_neuron = self.to_lava()
spike, self.v = lava_exchange.lava_neuron_forward(self.lava_neuron, x_seq, self.v)
return spike
else:
raise NotImplementedError(self.backend)
def extra_repr(self):
return super().extra_repr() + f', backend={self.backend}'
def to_lava(self):
return lava_exchange.to_lava_neuron(self)
def reset(self):
super().reset()
if self.lava_neuron is not None:
self.lava_neuron.current_state.zero_()
self.lava_neuron.voltage_state.zero_()
class ParametricLIFNode(BaseNode):
def __init__(self, init_tau: float = 2.0, decay_input: bool = True, v_threshold: float = 1.,
v_reset: float = 0., surrogate_function: Callable = surrogate.Sigmoid(),
detach_reset: bool = False):
"""
* :ref:`API in English <ParametricLIFNode.__init__-en>`
.. _ParametricLIFNode.__init__-cn:
:param init_tau: 膜电位时间常数的初始值
:type init_tau: float
:param decay_input: 输入是否会衰减
:type decay_input: bool
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
`Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks <https://arxiv.org/abs/2007.05785>`_
提出的 Parametric Leaky Integrate-and-Fire (PLIF)神经元模型，可以看作是带漏电的积分器。其阈下神经动力学方程为：
若 ``decay_input == True``:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] - (V[t-1] - V_{reset}))
若 ``decay_input == False``:
.. math::
V[t] = V[t-1] - \\frac{1}{\\tau}(V[t-1] - V_{reset}) + X[t]
其中 :math:`\\frac{1}{\\tau} = {\\rm Sigmoid}(w)`，:math:`w` 是可学习的参数。
* :ref:`中文API <ParametricLIFNode.__init__-cn>`
.. _ParametricLIFNode.__init__-en:
:param init_tau: the initial value of membrane time constant
:type init_tau: float
:param decay_input: whether the input will decay
:type decay_input: bool
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
The Parametric Leaky Integrate-and-Fire (PLIF) neuron, which is proposed by `Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks <https://arxiv.org/abs/2007.05785>`_ and can be seen as a leaky integrator.
The subthreshold neural dynamics of it is as followed:
IF ``decay_input == True``:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] - (V[t-1] - V_{reset}))
IF ``decay_input == False``:
.. math::
V[t] = V[t-1] - \\frac{1}{\\tau}(V[t-1] - V_{reset}) + X[t]
where :math:`\\frac{1}{\\tau} = {\\rm Sigmoid}(w)`, :math:`w` is a learnable parameter.
"""
assert isinstance(init_tau, float) and init_tau > 1.
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.decay_input = decay_input
init_w = - math.log(init_tau - 1.)
self.w = nn.Parameter(torch.as_tensor(init_w))
def extra_repr(self):
with torch.no_grad():
tau = 1. / self.w.sigmoid()
return super().extra_repr() + f', tau={tau}'
def neuronal_charge(self, x: torch.Tensor):
if self.decay_input:
if self.v_reset is None or self.v_reset == 0.:
self.v = self.v + (x - self.v) * self.w.sigmoid()
else:
self.v = self.v + (x - (self.v - self.v_reset)) * self.w.sigmoid()
else:
if self.v_reset is None or self.v_reset == 0.:
self.v = self.v * (1. - self.w.sigmoid()) + x
else:
self.v = self.v - (self.v - self.v_reset) * self.w.sigmoid() + x
class MultiStepParametricLIFNode(ParametricLIFNode):
def __init__(self, init_tau: float = 2., decay_input: bool = True, v_threshold: float = 1.,
v_reset: float = 0., surrogate_function: Callable = surrogate.Sigmoid(),
detach_reset: bool = False, backend='torch'):
"""
* :ref:`API in English <MultiStepParametricLIFNode.__init__-en>`
.. _MultiStepParametricLIFNode.__init__-cn:
:param init_tau: 膜电位时间常数的初始值
:type init_tau: float
:param decay_input: 输入是否会衰减
:type decay_input: bool
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
多步版本的 `Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks <https://arxiv.org/abs/2007.05785>`_
提出的 Parametric Leaky Integrate-and-Fire (PLIF)神经元模型，可以看作是带漏电的积分器。其阈下神经动力学方程为：
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] - (V[t-1] - V_{reset})
其中 :math:`\\frac{1}{\\tau} = {\\rm Sigmoid}(w)`，:math:`w` 是可学习的参数。
.. tip::
对于多步神经元，输入 ``x_seq.shape = [T, *]``，不仅可以使用 ``.v`` 和 ``.spike`` 获取 ``t = T - 1`` 时刻的电压和脉冲，还能够
使用 ``.v_seq`` 和 ``.spike_seq`` 获取完整的 ``T`` 个时刻的电压和脉冲。
.. tip::
阅读 :doc:`传播模式 <./clock_driven/10_propagation_pattern>` 以获取更多关于单步和多步传播的信息。
* :ref:`中文API <MultiStepParametricLIFNode.__init__-cn>`
.. _MultiStepParametricLIFNode.__init__-en:
:param init_tau: the initial value of membrane time constant
:type init_tau: float
:param decay_input: whether the input will decay
:type decay_input: bool
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
:param backend: use which backend, ``'torch'`` or ``'cupy'``. ``'cupy'`` is faster but only supports GPU
:type backend: str
The multi-step Parametric Leaky Integrate-and-Fire (PLIF) neuron, which is proposed by `Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks <https://arxiv.org/abs/2007.05785>`_ and can be seen as a leaky integrator.
The subthreshold neural dynamics of it is as followed:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] - (V[t-1] - V_{reset})
where :math:`\\frac{1}{\\tau} = {\\rm Sigmoid}(w)`, :math:`w` is a learnable parameter.
.. admonition:: Tip
:class: tip
The input for multi-step neurons are ``x_seq.shape = [T, *]``. We can get membrane potential and spike at
time-step ``t = T - 1`` by ``.v`` and ``.spike``. We can also get membrane potential and spike at all ``T``
time-steps by ``.v_seq`` and ``.spike_seq``.
.. admonition:: Tip
:class: tip
Read :doc:`Propagation Pattern <./clock_driven_en/10_propagation_pattern>` for more details about single-step
and multi-step propagation.
"""
super().__init__(init_tau, decay_input, v_threshold, v_reset, surrogate_function, detach_reset)
self.register_memory('v_seq', None)
check_backend(backend)
self.backend = backend
def forward(self, x_seq: torch.Tensor):
assert x_seq.dim() > 1
# x_seq.shape = [T, *]
if self.backend == 'torch':
spike_seq = []
self.v_seq = []
for t in range(x_seq.shape[0]):
spike_seq.append(super().forward(x_seq[t]).unsqueeze(0))
self.v_seq.append(self.v.unsqueeze(0))
spike_seq = torch.cat(spike_seq, 0)
self.v_seq = torch.cat(self.v_seq, 0)
return spike_seq
elif self.backend == 'cupy':
if isinstance(self.v, float):
v_init = self.v
self.v = torch.zeros_like(x_seq[0].data)
if v_init != 0.:
torch.fill_(self.v, v_init)
spike_seq, self.v_seq = neuron_kernel.MultiStepParametricLIFNodePTT.apply(
x_seq.flatten(1), self.v.flatten(0), self.w.sigmoid(), self.decay_input, self.v_threshold, self.v_reset,
self.detach_reset, self.surrogate_function.cuda_code)
spike_seq = spike_seq.reshape(x_seq.shape)
self.v_seq = self.v_seq.reshape(x_seq.shape)
self.v = self.v_seq[-1].clone()
return spike_seq
else:
raise NotImplementedError
def extra_repr(self):
return super().extra_repr() + f', backend={self.backend}'
class QIFNode(BaseNode):
def __init__(self, tau: float = 2., v_c: float = 0.8, a0: float = 1., v_threshold: float = 1., v_rest: float = 0.,
v_reset: float = -0.1,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False):
"""
* :ref:`API in English <QIFNode.__init__-en>`
.. _QIFNode.__init__-cn:
:param tau: 膜电位时间常数
:type tau: float
:param v_c: 关键电压
:type v_c: float
:param a0:
:type a0: float
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_rest: 静息电位
:type v_rest: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
Quadratic Integrate-and-Fire 神经元模型，一种非线性积分发放神经元模型，也是指数积分发放神经元(Exponential Integrate-and-Fire)的近似版本。其阈下神经动力学方程为：
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] + a_0 (V[t-1] - V_{rest})(V[t-1] - V_c))
* :ref:`中文API <QIFNode.__init__-cn>`
.. _QIFNode.__init__-en:
:param tau: membrane time constant
:type tau: float
:param v_c: critical voltage
:type v_c: float
:param a0:
:type a0: float
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_rest: resting potential
:type v_rest: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
The Quadratic Integrate-and-Fire neuron is a kind of nonlinear integrate-and-fire models and also an approximation of the Exponential Integrate-and-Fire model.
The subthreshold neural dynamics of it is as followed:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}(X[t] + a_0 (V[t-1] - V_{rest})(V[t-1] - V_c))
"""
assert isinstance(tau, float) and tau > 1.
if v_reset is not None:
assert v_threshold > v_reset
assert v_rest >= v_reset
assert a0 > 0
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.tau = tau
self.v_c = v_c
self.v_rest = v_rest
self.a0 = a0
def extra_repr(self):
return super().extra_repr() + f', tau={self.tau}, v_c={self.v_c}, a0={self.a0}, v_rest={self.v_rest}'
def neuronal_charge(self, x: torch.Tensor):
self.v = self.v + (x + self.a0 * (self.v - self.v_rest) * (self.v - self.v_c)) / self.tau
class EIFNode(BaseNode):
def __init__(self, tau: float = 2., delta_T: float = 1., theta_rh: float = .8, v_threshold: float = 1.,
v_rest: float = 0., v_reset: float = -0.1,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False):
"""
* :ref:`API in English <EIFNode.__init__-en>`
.. _EIFNode.__init__-cn:
:param tau: 膜电位时间常数
:type tau: float
:param delta_T: 陡峭度参数
:type delta_T: float
:param theta_rh: 基强度电压阈值
:type theta_rh: float
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_rest: 静息电位
:type v_rest: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
Exponential Integrate-and-Fire 神经元模型，一种非线性积分发放神经元模型，是由HH神经元模型(Hodgkin-Huxley model)简化后推导出的一维模型。在 :math:`\\Delta_T\\to 0` 时退化为LIF模型。其阈下神经动力学方程为：
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}\\left(X[t] - (V[t-1] - V_{rest}) + \\Delta_T\\exp\\left(\\frac{V[t-1] - \\theta_{rh}}{\\Delta_T}\\right)\\right)
* :ref:`中文API <EIFNode.__init__-cn>`
.. _EIFNode.__init__-en:
:param tau: membrane time constant
:type tau: float
:param delta_T: sharpness parameter
:type delta_T: float
:param theta_rh: rheobase threshold
:type theta_rh: float
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_rest: resting potential
:type v_rest: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
The Exponential Integrate-and-Fire neuron is a kind of nonlinear integrate-and-fire models and also an one-dimensional model derived from the Hodgkin-Huxley model. It degenerates to the LIF model when :math:`\\Delta_T\\to 0`.
The subthreshold neural dynamics of it is as followed:
.. math::
V[t] = V[t-1] + \\frac{1}{\\tau}\\left(X[t] - (V[t-1] - V_{rest}) + \\Delta_T\\exp\\left(\\frac{V[t-1] - \\theta_{rh}}{\\Delta_T}\\right)\\right)
"""
assert isinstance(tau, float) and tau > 1.
if v_reset is not None:
assert v_threshold > v_reset
assert v_rest >= v_reset
assert delta_T > 0
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.tau = tau
self.delta_T = delta_T
self.v_rest = v_rest
self.theta_rh = theta_rh
def extra_repr(self):
return super().extra_repr() + f', tau={self.tau}, delta_T={self.delta_T}, theta_rh={self.theta_rh}'
def neuronal_charge(self, x: torch.Tensor):
with torch.no_grad():
if not isinstance(self.v, torch.Tensor):
self.v = torch.as_tensor(self.v, device=x.device)
self.v = self.v + (x + self.v_rest - self.v + self.delta_T * torch.exp(
(self.v - self.theta_rh) / self.delta_T)) / self.tau
class MultiStepEIFNode(EIFNode):
def __init__(self, tau: float = 2., delta_T: float = 1., theta_rh: float = .8, v_threshold: float = 1.,
v_rest: float = 0., v_reset: float = -0.1,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False, backend='torch'):
"""
* :ref:`API in English <MultiStepEIFNode.__init__-en>`
.. _MultiStepEIFNode.__init__-cn:
::param tau: 膜电位时间常数
:type tau: float
:param delta_T: 陡峭度参数
:type delta_T: float
:param theta_rh: 基强度电压阈值
:type theta_rh: float
:param v_threshold: 神经元的阈值电压
:type v_threshold: float
:param v_rest: 静息电位
:type v_rest: float
:param v_reset: 神经元的重置电压。如果不为 ``None``，当神经元释放脉冲后，电压会被重置为 ``v_reset``；
如果设置为 ``None``，则电压会被减去 ``v_threshold``
:type v_reset: float
:param surrogate_function: 反向传播时用来计算脉冲函数梯度的替代函数
:type surrogate_function: Callable
:param detach_reset: 是否将reset过程的计算图分离
:type detach_reset: bool
多步版本的 :class:`spikingjelly.clock_driven.neuron.EIFNode`。
.. tip::
对于多步神经元，输入 ``x_seq.shape = [T, *]``，不仅可以使用 ``.v`` 和 ``.spike`` 获取 ``t = T - 1`` 时刻的电压和脉冲，还能够
使用 ``.v_seq`` 和 ``.spike_seq`` 获取完整的 ``T`` 个时刻的电压和脉冲。
.. tip::
阅读 :doc:`传播模式 <./clock_driven/10_propagation_pattern>` 以获取更多关于单步和多步传播的信息。
* :ref:`中文API <MultiStepEIFNode.__init__-cn>`
.. _MultiStepEIFNode.__init__-en:
:param tau: membrane time constant
:type tau: float
:param delta_T: sharpness parameter
:type delta_T: float
:param theta_rh: rheobase threshold
:type theta_rh: float
:param v_threshold: threshold voltage of neurons
:type v_threshold: float
:param v_rest: resting potential
:type v_rest: float
:param v_reset: reset voltage of neurons. If not ``None``, voltage of neurons that just fired spikes will be set to
``v_reset``. If ``None``, voltage of neurons that just fired spikes will subtract ``v_threshold``
:type v_reset: float
:param surrogate_function: surrogate function for replacing gradient of spiking functions during back-propagation
:type surrogate_function: Callable
:param detach_reset: whether detach the computation graph of reset
:type detach_reset: bool
:param backend: use which backend, ``'torch'`` or ``'cupy'``. ``'cupy'`` is faster but only supports GPU
:type backend: str
.. admonition:: Tip
:class: tip
The input for multi-step neurons are ``x_seq.shape = [T, *]``. We can get membrane potential and spike at
time-step ``t = T - 1`` by ``.v`` and ``.spike``. We can also get membrane potential and spike at all ``T``
time-steps by ``.v_seq`` and ``.spike_seq``.
.. admonition:: Tip
:class: tip
Read :doc:`Propagation Pattern <./clock_driven_en/10_propagation_pattern>` for more details about single-step
and multi-step propagation.
"""
super().__init__(tau, delta_T, theta_rh, v_threshold, v_rest, v_reset,
surrogate_function, detach_reset)
self.register_memory('v_seq', None)
check_backend(backend)
self.backend = backend
def forward(self, x_seq: torch.Tensor):
assert x_seq.dim() > 1
# x_seq.shape = [T, *]
if self.backend == 'torch':
spike_seq = []
self.v_seq = []
for t in range(x_seq.shape[0]):
spike_seq.append(super().forward(x_seq[t]).unsqueeze(0))
self.v_seq.append(self.v.unsqueeze(0))
spike_seq = torch.cat(spike_seq, 0)
self.v_seq = torch.cat(self.v_seq, 0)
return spike_seq
elif self.backend == 'cupy':
if isinstance(self.v, float):
v_init = self.v
self.v = torch.zeros_like(x_seq[0].data)
if v_init != 0.:
torch.fill_(self.v, v_init)
spike_seq, self.v_seq = neuron_kernel.MultiStepEIFNodePTT.apply(
x_seq.flatten(1), self.v.flatten(0), self.tau, self.v_threshold, self.v_reset, self.v_rest,
self.theta_rh, self.delta_T, self.detach_reset, self.surrogate_function.cuda_code)
spike_seq = spike_seq.reshape(x_seq.shape)
self.v_seq = self.v_seq.reshape(x_seq.shape)
self.v = self.v_seq[-1].clone()
return spike_seq
else:
raise NotImplementedError
def extra_repr(self):
return super().extra_repr() + f', backend={self.backend}'
class GeneralNode(BaseNode):
def __init__(self, a: float or torch.Tensor, b: float or torch.Tensor, c: float or torch.Tensor = 0.,
v_threshold: float = 1., v_reset: float = 0.,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False):
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.a = self.register_buffer('a', torch.as_tensor(a))
self.b = self.register_buffer('b', torch.as_tensor(b))
self.c = self.register_buffer('c', torch.as_tensor(c))
def neuronal_charge(self, x: torch.Tensor):
self.v = self.a * self.v + self.b * x + self.c
class MultiStepGeneralNode(GeneralNode):
def __init__(self, a: float, b: float, c: float, v_threshold: float = 1., v_reset: float = 0.,
surrogate_function: Callable = surrogate.Sigmoid(), detach_reset: bool = False, backend='torch'):
super().__init__(v_threshold, v_reset, surrogate_function, detach_reset)
self.register_memory('v_seq', None)
check_backend(backend)
self.backend = backend
def forward(self, x_seq: torch.Tensor):
assert x_seq.dim() > 1
# x_seq.shape = [T, *]
if self.backend == 'torch':
spike_seq = []
self.v_seq = []
for t in range(x_seq.shape[0]):
spike_seq.append(super().forward(x_seq[t]).unsqueeze(0))
self.v_seq.append(self.v.unsqueeze(0))
spike_seq = torch.cat(spike_seq, 0)
self.v_seq = torch.cat(self.v_seq, 0)
return spike_seq
elif self.backend == 'cupy':
if isinstance(self.v, float):
v_init = self.v
self.v = torch.zeros_like(x_seq[0].data)
if v_init != 0.:
torch.fill_(self.v, v_init)
raise NotImplementedError
spike_seq = spike_seq.reshape(x_seq.shape)
self.v_seq = self.v_seq.reshape(x_seq.shape)
self.v = self.v_seq[-1].clone()
return spike_seq
else:
raise NotImplementedError
def extra_repr(self):
return super().extra_repr() + f', backend={self.backend}'
class LIAFNode(LIFNode):
def __init__(self, act: Callable, threshold_related: bool, *args, **kwargs):
"""
:param act: the activation function
:type act: Callable
:param threshold_related: whether the neuron uses threshold related (TR mode). If true, `y = act(h - v_th)`,
otherwise `y = act(h)`
:type threshold_related: bool
Other parameters in `*args, **kwargs` are same with :class:`LIFNode`.
The LIAF neuron proposed in `LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing <https://arxiv.org/abs/2011.06176>`_.
.. admonition:: Warning
:class: warning
The outputs of this neuron are not binary spikes.
"""
super().__init__(*args, **kwargs)
self.act = act
self.threshold_related = threshold_related
def forward(self, x: torch.Tensor):
self.neuronal_charge(x)
if self.threshold_related:
y = self.act(self.v - self.v_threshold)
else:
y = self.act(self.v)
spike = self.neuronal_fire()
self.neuronal_reset(spike)
return y
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