Merge pull request brainpy#522 from chaoming0625/updates

[delay] rewrite previous delay APIs so that they are compatible with new brainpy version

brainpy/_src/delay.py

@@ -369,7 +369,7 @@ def update(
         else:
           self.data[0] = latest_value
 
-  def reset_state(self, batch_size: int = None):
+  def reset_state(self, batch_size: int = None, **kwargs):
     """Reset the delay data.
     """
     # initialize delay data
@@ -439,7 +439,7 @@ def __init__(
                      name=name,
                      mode=mode)
 
-  def reset_state(self, batch_size: int = None):
+  def reset_state(self, batch_size: int = None, **kwargs):
     """Reset the delay data.
     """
     self.target.value = variable_(self.target_init, self.target.size_without_batch, batch_size)
@@ -476,9 +476,9 @@ def reset_state(self, *args, **kwargs):
     pass
 
 
-def init_delay_by_return(info: Union[bm.Variable, ReturnInfo]) -> Delay:
+def init_delay_by_return(info: Union[bm.Variable, ReturnInfo], initial_delay_data=None) -> Delay:
   if isinstance(info, bm.Variable):
-    return VarDelay(info)
+    return VarDelay(info, init=initial_delay_data)
 
   elif isinstance(info, ReturnInfo):
     # batch size
@@ -510,6 +510,6 @@ def init_delay_by_return(info: Union[bm.Variable, ReturnInfo]) -> Delay:
 
     # variable
     target = bm.Variable(init, batch_axis=batch_axis, axis_names=info.axis_names)
-    return DataDelay(target, data_init=info.data)
+    return DataDelay(target, data_init=info.data, init=initial_delay_data)
   else:
     raise TypeError

brainpy/_src/dyn/projections/aligns.py

@@ -27,6 +27,9 @@ def update(self, x):
     else:
       return x >> self.syn >> self.delay
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class _AlignPost(DynamicalSystem):
   def __init__(self,
@@ -39,6 +42,9 @@ def __init__(self,
   def update(self, *args, **kwargs):
     self.out.bind_cond(self.syn(*args, **kwargs))
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class _AlignPreMg(DynamicalSystem):
   def __init__(self, access, syn):
@@ -49,6 +55,9 @@ def __init__(self, access, syn):
   def update(self, *args, **kwargs):
     return self.syn(self.access())
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 def _get_return(return_info):
   if isinstance(return_info, bm.Variable):
@@ -132,6 +141,9 @@ def update(self, x):
     self.refs['out'].bind_cond(current)
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPostMg1(Projection):
   r"""Synaptic projection which defines the synaptic computation with the dimension of postsynaptic neuron group.
@@ -224,6 +236,9 @@ def update(self, x):
     self.refs['syn'].add_current(current)  # synapse post current
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPostMg2(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of postsynaptic neuron group.
@@ -352,6 +367,9 @@ def update(self):
     self.refs['syn'].add_current(current)  # synapse post current
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPost1(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of postsynaptic neuron group.
@@ -438,6 +456,9 @@ def update(self, x):
     self.refs['syn'].add_current(current)
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPost2(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of postsynaptic neuron group.
@@ -561,6 +582,9 @@ def update(self):
     self.refs['out'].bind_cond(g)  # synapse post current
     return g
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPreMg1(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of presynaptic neuron group.
@@ -686,6 +710,9 @@ def update(self, x=None):
     self.refs['out'].bind_cond(current)
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPreMg2(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of presynaptic neuron group.
@@ -814,6 +841,9 @@ def update(self):
     self.refs['out'].bind_cond(current)
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPre1(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of presynaptic neuron group.
@@ -933,6 +963,9 @@ def update(self, x=None):
     self.refs['out'].bind_cond(current)
     return current
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
 
 class ProjAlignPre2(Projection):
   """Synaptic projection which defines the synaptic computation with the dimension of presynaptic neuron group.
@@ -1052,4 +1085,7 @@ def update(self):
     spk = self.refs['delay'].at(self.name)
     g = self.comm(self.syn(spk))
     self.refs['out'].bind_cond(g)
-    return g
+    return g
+
+  def reset_state(self, *args, **kwargs):
+    pass

brainpy/_src/dyn/projections/others.py

@@ -54,6 +54,9 @@ def __init__(
     self.freq = check.is_float(freq, min_bound=0., allow_int=True)
     self.weight = check.is_float(weight, allow_int=True)
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
   def update(self):
     p = self.freq * share['dt'] / 1e3
     a = self.num_input * p

brainpy/_src/dyn/projections/plasticity.py

@@ -174,6 +174,9 @@ def __init__(
     self.A1 = parameter(A1, sizes=self.pre_num)
     self.A2 = parameter(A2, sizes=self.post_num)
 
+  def reset_state(self, *args, **kwargs):
+    pass
+
   def _init_trace(
       self,
       target: DynamicalSystem,

brainpy/_src/dyn/synapses/delay_couplings.py

@@ -191,7 +191,7 @@ def __init__(
   def update(self):
     # delays
     axis = self.coupling_var1.ndim
-    delay_var: bm.LengthDelay = self.get_delay_var(f'delay_{id(self.delay_var)}')[0]
+    delay_var = self.get_delay_var(f'delay_{id(self.delay_var)}')
     if self.delay_steps is None:
       diffusive = (jnp.expand_dims(self.coupling_var1.value, axis=axis) -
                    jnp.expand_dims(self.coupling_var2.value, axis=axis - 1))
@@ -201,13 +201,13 @@ def update(self):
         indices = (slice(None, None, None), jnp.arange(self.coupling_var1.size),)
       else:
         indices = (jnp.arange(self.coupling_var1.size),)
-      f = vmap(lambda steps: delay_var(steps, *indices), in_axes=1)  # (..., pre.num)
+      f = vmap(lambda steps: delay_var.retrieve(steps, *indices), in_axes=1)  # (..., pre.num)
       delays = f(self.delay_steps)  # (..., post.num, pre.num)
       diffusive = (jnp.moveaxis(bm.as_jax(delays), axis - 1, axis) -
                    jnp.expand_dims(self.coupling_var2.value, axis=axis - 1))  # (..., pre.num, post.num)
       diffusive = (self.conn_mat * diffusive).sum(axis=axis - 1)
     elif self.delay_type == 'int':
-      delayed_data = delay_var(self.delay_steps)  # (..., pre.num)
+      delayed_data = delay_var.retrieve(self.delay_steps)  # (..., pre.num)
       diffusive = (jnp.expand_dims(delayed_data, axis=axis) -
                    jnp.expand_dims(self.coupling_var2.value, axis=axis - 1))  # (..., pre.num, post.num)
       diffusive = (self.conn_mat * diffusive).sum(axis=axis - 1)
@@ -276,19 +276,19 @@ def __init__(
   def update(self):
     # delay function
     axis = self.coupling_var.ndim
-    delay_var: bm.LengthDelay = self.get_delay_var(f'delay_{id(self.delay_var)}')[0]
+    delay_var = self.get_delay_var(f'delay_{id(self.delay_var)}')
     if self.delay_steps is None:
       additive = self.coupling_var @ self.conn_mat
     elif self.delay_type == 'array':
       if isinstance(self.mode, bm.TrainingMode):
         indices = (slice(None, None, None), jnp.arange(self.coupling_var.size),)
       else:
         indices = (jnp.arange(self.coupling_var.size),)
-      f = vmap(lambda steps: delay_var(steps, *indices), in_axes=1)  # (.., pre.num,)
+      f = vmap(lambda steps: delay_var.retrieve(steps, *indices), in_axes=1)  # (.., pre.num,)
       delays = f(self.delay_steps)  # (..., post.num, pre.num)
       additive = (self.conn_mat * jnp.moveaxis(delays, axis - 1, axis)).sum(axis=axis - 1)
     elif self.delay_type == 'int':
-      delayed_var = delay_var(self.delay_steps)  # (..., pre.num)
+      delayed_var = delay_var.retrieve(self.delay_steps)  # (..., pre.num)
       additive = delayed_var @ self.conn_mat
     else:
       raise ValueError

brainpy/_src/dynold/synapses/abstract_models.py

@@ -124,7 +124,7 @@ def reset_state(self, batch_size=None):
   def update(self, pre_spike=None):
     # pre-synaptic spikes
     if pre_spike is None:
-      pre_spike = self.get_delay_data(f"{self.pre.name}.spike", delay_step=self.delay_step)
+      pre_spike = self.get_delay_data(f"{self.pre.name}.spike", self.delay_step)
     pre_spike = bm.as_jax(pre_spike)
     if self.stop_spike_gradient:
       pre_spike = jax.lax.stop_gradient(pre_spike)