Merge pull request brainpy#524 from chaoming0625/updates

[math] the interface for operator registration

README.md

@@ -77,7 +77,7 @@ We provide a Binder environment for BrainPy. You can use the following button to
 - **[BrainPy](https://github.com/brainpy/BrainPy)**: The solution for the general-purpose brain dynamics programming. 
 - **[brainpy-examples](https://github.com/brainpy/examples)**: Comprehensive examples of BrainPy computation. 
 - **[brainpy-datasets](https://github.com/brainpy/datasets)**: Neuromorphic and Cognitive Datasets for Brain Dynamics Modeling.
-- [第一届神经计算建模与编程培训班 (BrainPy First Training Course on Neural Modeling and Programming)](https://github.com/brainpy/1st-neural-modeling-and-programming-course)
+- [第一届神经计算建模与编程培训班 (First Training Course on Neural Modeling and Programming)](https://github.com/brainpy/1st-neural-modeling-and-programming-course)
 
 
 ## Citing 

brainpy/_src/dnn/linear.py

@@ -3,6 +3,7 @@
 
 from typing import Dict, Optional, Union, Callable
 
+import numba
 import jax
 import numpy as np
 import jax.numpy as jnp
@@ -227,6 +228,45 @@ def update(self, x):
     return x
 
 
+def event_mm(pre_spike, post_inc, weight, w_min, w_max):
+  return weight
+
+
+@numba.njit
+def event_mm_imp(outs, ins):
+  pre_spike, post_inc, weight, w_min, w_max = ins
+  w_min = w_min[()]
+  w_max = w_max[()]
+  outs = outs
+  outs.fill(weight)
+  for i in range(pre_spike.shape[0]):
+    if pre_spike[i]:
+      outs[i] = np.clip(outs[i] + post_inc, w_min, w_max)
+
+
+event_left_mm = bm.CustomOpByNumba(event_mm, event_mm_imp, multiple_results=False)
+
+
+def event_mm2(post_spike, pre_inc, weight, w_min, w_max):
+  return weight
+
+
+@numba.njit
+def event_mm_imp2(outs, ins):
+  post_spike, pre_inc, weight, w_min, w_max = ins
+  w_min = w_min[()]
+  w_max = w_max[()]
+  outs = outs
+  outs.fill(weight)
+  for j in range(post_spike.shape[0]):
+    if post_spike[j]:
+      outs[:, j] = np.clip(outs[:, j] + pre_inc, w_min, w_max)
+
+
+event_right_mm = bm.CustomOpByNumba(event_mm2, event_mm_imp2, multiple_results=False)
+
+
+
 class AllToAll(Layer, SupportSTDP):
   """Synaptic matrix multiplication with All2All connections.
 
@@ -289,20 +329,15 @@ def update(self, pre_val):
         post_val = pre_val @ self.weight
     return post_val
 
-  def update_STDP(self, dW, constraints=None):
-    if isinstance(self.weight, float):
-      raise ValueError(f'Cannot update the weight of a constant node.')
-    if not isinstance(dW, (bm.ndarray, jnp.ndarray, np.ndarray)):
-      raise ValueError(f'"delta_weight" must be a array, but got {type(dW)}')
-    if self.weight.shape != dW.shape:
-      raise ValueError(f'The shape of delta_weight {dW.shape} '
-                       f'should be the same as the shape of weight {self.weight.shape}.')
+  def stdp_update_on_pre(self, pre_spike, trace, w_min=None, w_max=None):
     if not isinstance(self.weight, bm.Variable):
       self.tracing_variable('weight', self.weight, self.weight.shape)
-    self.weight += dW
-    if constraints is not None:
-      self.weight.value = constraints(self.weight)
+    self.weight.value = event_left_mm(pre_spike, trace, self.weight, w_min, w_max)
 
+  def stdp_update_on_post(self, post_spike, trace, w_min=None, w_max=None):
+    if not isinstance(self.weight, bm.Variable):
+      self.tracing_variable('weight', self.weight, self.weight.shape)
+    self.weight.value = event_right_mm(post_spike, trace, self.weight, w_min, w_max)
 
 
 class OneToOne(Layer, SupportSTDP):
@@ -338,21 +373,6 @@ def __init__(
   def update(self, pre_val):
     return pre_val * self.weight
 
-  def update_STDP(self, dW, constraints=None):
-    if isinstance(self.weight, float):
-      raise ValueError(f'Cannot update the weight of a constant node.')
-    if not isinstance(dW, (bm.ndarray, jnp.ndarray, np.ndarray)):
-      raise ValueError(f'"delta_weight" must be a array, but got {type(dW)}')
-    dW = dW.sum(axis=0)
-    if self.weight.shape != dW.shape:
-      raise ValueError(f'The shape of delta_weight {dW.shape} '
-                       f'should be the same as the shape of weight {self.weight.shape}.')
-    if not isinstance(self.weight, bm.Variable):
-      self.tracing_variable('weight', self.weight, self.weight.shape)
-    self.weight += dW
-    if constraints is not None:
-      self.weight.value = constraints(self.weight)
-
 
 class MaskedLinear(Layer, SupportSTDP):
   r"""Synaptic matrix multiplication with masked dense computation.

brainpy/_src/dyn/others/input.py

@@ -228,9 +228,5 @@ def update(self):
   def return_info(self):
     return self.spike
 
-  def reset_state(self, batch_size=None, **kwargs):
-    self.spike = variable_(partial(jnp.zeros, dtype=self.spk_type),
-                           self.varshape,
-                           batch_size,
-                           axis_names=self.sharding,
-                           batch_axis_name=bm.sharding.BATCH_AXIS)
+  def reset_state(self, batch_or_mode=None, **kwargs):
+    self.spike = self.init_variable(partial(jnp.zeros, dtype=self.spk_type), batch_or_mode)

brainpy/_src/dyn/projections/plasticity.py

@@ -4,7 +4,6 @@
 from brainpy._src.delay import register_delay_by_return
 from brainpy._src.dyn.synapses.abstract_models import Expon
 from brainpy._src.dynsys import DynamicalSystem, Projection
-from brainpy._src.initialize import parameter
 from brainpy._src.mixin import (JointType, ParamDescriber, SupportAutoDelay,
                                 BindCondData, AlignPost, SupportSTDP)
 from brainpy.types import ArrayType
@@ -111,7 +110,8 @@ def run(i, I_pre, I_post):
     A1: float. The increment of :math:`A_{pre}` produced by a spike. Must be a positive value.
     A2: float. The increment of :math:`A_{post}` produced by a spike. Must be a positive value.
     W_max: float. The maximum weight.
-    pre: DynamicalSystem. The pre-synaptic neuron group. 
+    W_min: float. The minimum weight.
+    pre: DynamicalSystem. The pre-synaptic neuron group.
     delay: int, float. The pre spike delay length. (ms)
     syn: DynamicalSystem. The synapse model.
     comm: DynamicalSystem. The communication model, for example, dense or sparse connection layers.
@@ -135,6 +135,7 @@ def __init__(
       A1: Union[float, ArrayType, Callable] = 0.96,
       A2: Union[float, ArrayType, Callable] = 0.53,
       W_max: Optional[float] = None,
+      W_min: Optional[float] = None,
       # others
       out_label: Optional[str] = None,
       name: Optional[str] = None,
@@ -144,21 +145,21 @@ def __init__(
 
     # synaptic models
     check.is_instance(pre, JointType[DynamicalSystem, SupportAutoDelay])
-    check.is_instance(syn, ParamDescriber[DynamicalSystem])
     check.is_instance(comm, JointType[DynamicalSystem, SupportSTDP])
+    check.is_instance(syn, ParamDescriber[DynamicalSystem])
     check.is_instance(out, ParamDescriber[JointType[DynamicalSystem, BindCondData]])
     check.is_instance(post, DynamicalSystem)
     self.pre_num = pre.num
     self.post_num = post.num
     self.comm = comm
-    self.syn = syn
+    self._is_align_post = issubclass(syn.cls, AlignPost)
 
     # delay initialization
     delay_cls = register_delay_by_return(pre)
     delay_cls.register_entry(self.name, delay)
 
     # synapse and output initialization
-    if issubclass(syn.cls, AlignPost):
+    if self._is_align_post:
       syn_cls, out_cls = align_post_add_bef_update(out_label, syn_desc=syn, out_desc=out, post=post,
                                                    proj_name=self.name)
     else:
@@ -171,24 +172,27 @@ def __init__(
     self.refs['delay'] = delay_cls
     self.refs['syn'] = syn_cls  # invisible to ``self.node()``
     self.refs['out'] = out_cls  # invisible to ``self.node()``
+    self.refs['comm'] = comm
 
     # tracing pre-synaptic spikes using Exponential model
     self.refs['pre_trace'] = _init_trace_by_align_pre2(pre, delay, Expon.desc(pre.num, tau=tau_s))
+
     # tracing post-synaptic spikes using Exponential model
     self.refs['post_trace'] = _init_trace_by_align_pre2(post, None, Expon.desc(post.num, tau=tau_t))
 
     # synapse parameters
     self.W_max = W_max
-    self.tau_s = parameter(tau_s, sizes=self.pre_num)
-    self.tau_t = parameter(tau_t, sizes=self.post_num)
-    self.A1 = parameter(A1, sizes=self.pre_num)
-    self.A2 = parameter(A2, sizes=self.post_num)
+    self.W_min = W_min
+    self.tau_s = tau_s
+    self.tau_t = tau_t
+    self.A1 = A1
+    self.A2 = A2
 
   def update(self):
     # pre-synaptic spikes
     pre_spike = self.refs['delay'].at(self.name)  # spike
     # pre-synaptic variables
-    if issubclass(self.syn.cls, AlignPost):
+    if self._is_align_post:
       # For AlignPost, we need "pre spikes @ comm matrix" for computing post-synaptic conductance
       x = pre_spike
     else:
@@ -201,19 +205,17 @@ def update(self):
     post_spike = self.refs['post'].spike
 
     # weight updates
-    Apre = self.refs['pre_trace'].g
     Apost = self.refs['post_trace'].g
-    delta_w = - bm.outer(pre_spike, Apost * self.A2) + bm.outer(Apre * self.A1, post_spike)
-    self.comm.update_STDP(delta_w, constraints=self._weight_clip)
+    self.comm.stdp_update_on_pre(pre_spike, -Apost * self.A2, self.W_min, self.W_max)
+    Apre = self.refs['pre_trace'].g
+    self.comm.stdp_update_on_post(post_spike, Apre * self.A1, self.W_min, self.W_max)
 
-    # currents
+    # synaptic currents
     current = self.comm(x)
-    if issubclass(self.syn.cls, AlignPost):
+    if self._is_align_post:
       self.refs['syn'].add_current(current)  # synapse post current
     else:
       self.refs['out'].bind_cond(current)  # align pre
     return current
 
-  def _weight_clip(self, w):
-    return w if self.W_max is None else bm.minimum(w, self.W_max)
 

brainpy/_src/dyn/projections/tests/test_STDP.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
-import os
-os.environ['JAX_TRACEBACK_FILTERING'] = 'off'
+import matplotlib.pyplot as plt
+import numpy as np
 from absl.testing import parameterized
 
 import brainpy as bp
@@ -20,15 +20,18 @@ def __init__(self, num_pre, num_post):
         self.syn = bp.dyn.STDP_Song2000(
           pre=self.pre,
           delay=1.,
-          comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(1, pre=self.pre.num, post=self.post.num),
-                                     weight=lambda s: bm.Variable(bm.random.rand(*s) * 0.1)),
+          # comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(1, pre=self.pre.num, post=self.post.num),
+          #                            weight=bp.init.Uniform(0., 0.1)),
+          comm=bp.dnn.AllToAll(self.pre.num, self.post.num, weight=bp.init.Uniform(.1, 0.1)),
           syn=bp.dyn.Expon.desc(self.post.varshape, tau=5.),
           out=bp.dyn.COBA.desc(E=0.),
           post=self.post,
           tau_s=16.8,
           tau_t=33.7,
           A1=0.96,
           A2=0.53,
+          W_min=0.,
+          W_max=1.
         )
 
       def update(self, I_pre, I_post):
@@ -39,7 +42,7 @@ def update(self, I_pre, I_post):
         Apre = self.syn.refs['pre_trace'].g
         Apost = self.syn.refs['post_trace'].g
         current = self.post.sum_inputs(self.post.V)
-        return self.pre.spike, self.post.spike, conductance, Apre, Apost, current, self.syn.comm.weight
+        return self.pre.spike, self.post.spike, conductance, Apre, Apost, current, self.syn.comm.weight.flatten()
 
     duration = 300.
     I_pre = bp.inputs.section_input([0, 30, 0, 30, 0, 30, 0, 30, 0, 30, 0, 30, 0],
@@ -53,7 +56,14 @@ def run(i, I_pre, I_post):
       pre_spike, post_spike, g, Apre, Apost, current, W = net.step_run(i, I_pre, I_post)
       return pre_spike, post_spike, g, Apre, Apost, current, W
 
-    indices = bm.arange(0, duration, bm.dt)
-    bm.for_loop(run, [indices, I_pre, I_post], jit=True)
-    bm.clear_buffer_memory()
+    indices = np.arange(int(duration / bm.dt))
+    pre_spike, post_spike, g, Apre, Apost, current, W = bm.for_loop(run, [indices, I_pre, I_post])
+
+    fig, gs = bp.visualize.get_figure(4, 1, 3, 10)
+    bp.visualize.line_plot(indices, g, ax=fig.add_subplot(gs[0, 0]))
+    bp.visualize.line_plot(indices, Apre, ax=fig.add_subplot(gs[1, 0]))
+    bp.visualize.line_plot(indices, Apost, ax=fig.add_subplot(gs[2, 0]))
+    bp.visualize.line_plot(indices, W, ax=fig.add_subplot(gs[3, 0]))
+    plt.show()
 
+    bm.clear_buffer_memory()

brainpy/_src/dyn/rates/tests/test_reservoir.py

@@ -15,7 +15,7 @@ class Test_Reservoir(parameterized.TestCase):
     def test_Reservoir(self, mode):
         bm.random.seed()
         input = bm.random.randn(10, 3)
-        layer = bp.dnn.Reservoir(input_shape=3,
+        layer = bp.dyn.Reservoir(input_shape=3,
                                  num_out=5,
                                  mode=mode)
         if mode in [bm.NonBatchingMode()]: