Support initializing a Variable by data shape

brainpy/dyn/base.py

@@ -756,8 +756,8 @@ def __init__(
 
   def __repr__(self):
     names = self.__class__.__name__
-    return (f'{names}(name={self.name}, mode={self.mode}, '
-            f'{" " * len(names)} pre={self.pre}, '
+    return (f'{names}(name={self.name}, mode={self.mode}, \n'
+            f'{" " * len(names)} pre={self.pre}, \n'
             f'{" " * len(names)} post={self.post})')
 
   def check_pre_attrs(self, *attrs):

brainpy/dyn/layers/dropout.py

@@ -15,10 +15,6 @@ class Dropout(DynamicalSystem):
   In training, to compensate for the fraction of input values dropped (`rate`),
   all surviving values are multiplied by `1 / (1 - rate)`.
 
-  The parameter `shared_axes` allows to specify a list of axes on which
-  the mask will be shared: we will use size 1 on those axes for dropout mask
-  and broadcast it. Sharing reduces randomness, but can save memory.
-
   This layer is active only during training (`mode='train'`). In other
   circumstances it is a no-op.
 

brainpy/dyn/synapses/gap_junction.py

@@ -29,8 +29,8 @@ def __init__(
                                       conn=conn,
                                       name=name)
     # checking
-    self.check_pre_attrs('V', 'spike')
-    self.check_post_attrs('V', 'input', 'spike')
+    self.check_pre_attrs('V')
+    self.check_post_attrs('V', 'input')
 
     # assert isinstance(self.output, _NullSynOut)
     # assert isinstance(self.stp, _NullSynSTP)

brainpy/math/jaxarray.py

@@ -885,10 +885,42 @@ def __jax_array__(self):
 
 class Variable(JaxArray):
   """The pointer to specify the dynamical variable.
+
+  Initializing an instance of ``Variable`` by two ways:
+
+  >>> import brainpy.math as bm
+  >>> # 1. init a Variable by the concreate data
+  >>> v1 = bm.Variable(bm.zeros(10))
+  >>> # 2. init a Variable by the data shape
+  >>> v2 = bm.Variable(10)
+
+  Note that when initializing a `Variable` by the data shape,
+  all values in this `Variable` will be initialized as zeros.
+
+  Parameters
+  ----------
+  value_or_size: Shape, Array
+    The value or the size of the value.
+  dtype:
+    The type of the data.
+  batch_axis: optional, int
+    The batch axis.
   """
   __slots__ = ('_value', '_batch_axis')
 
-  def __init__(self, value, dtype=None, batch_axis: int = None):
+  def __init__(
+      self,
+      value_or_size,
+      dtype=None,
+      batch_axis: int = None
+  ):
+    if isinstance(value_or_size, int):
+      value = jnp.zeros(value_or_size, dtype=dtype)
+    elif isinstance(value_or_size, (tuple, list)) and all([isinstance(s, int) for s in value_or_size]):
+      value = jnp.zeros(value_or_size, dtype=dtype)
+    else:
+      value = value_or_size
+
     super(Variable, self).__init__(value, dtype=dtype)
 
     # check batch axis
@@ -1464,17 +1496,17 @@ class TrainVar(Variable):
   """
   __slots__ = ('_value', '_batch_axis')
 
-  def __init__(self, value, dtype=None, batch_axis: int = None):
-    super(TrainVar, self).__init__(value, dtype=dtype, batch_axis=batch_axis)
+  def __init__(self, value_or_size, dtype=None, batch_axis: int = None):
+    super(TrainVar, self).__init__(value_or_size, dtype=dtype, batch_axis=batch_axis)
 
 
 class Parameter(Variable):
   """The pointer to specify the parameter.
   """
   __slots__ = ('_value', '_batch_axis')
 
-  def __init__(self, value, dtype=None, batch_axis: int = None):
-    super(Parameter, self).__init__(value, dtype=dtype, batch_axis=batch_axis)
+  def __init__(self, value_or_size, dtype=None, batch_axis: int = None):
+    super(Parameter, self).__init__(value_or_size, dtype=dtype, batch_axis=batch_axis)
 
 
 register_pytree_node(JaxArray,

brainpy/math/tests/test_jaxarray.py

@@ -40,3 +40,14 @@ def test_none(self):
       ee = a + e
 
 
+class TestVariable(unittest.TestCase):
+  def test_variable_init(self):
+    self.assertTrue(
+      bm.array_equal(bm.Variable(bm.zeros(10)),
+                     bm.Variable(10))
+    )
+    bm.random.seed(123)
+    self.assertTrue(
+      not bm.array_equal(bm.Variable(bm.random.rand(10)),
+                         bm.Variable(10))
+    )

examples/simulation/Fazli_2022_gj_coupled_bursting_pituitary_cells.py

@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+
+
+""""
+Implementation of the paper:
+
+- Fazli, Mehran, and Richard Bertram. "Network Properties of Electrically
+  Coupled Bursting Pituitary Cells." Frontiers in Endocrinology 13 (2022).
+"""
+
+import brainpy as bp
+import brainpy.math as bm
+
+
+class PituitaryCell(bp.NeuGroup):
+  def __init__(self, size, name=None):
+    super(PituitaryCell, self).__init__(size, name=name)
+
+    # parameter values
+    self.vn = -5
+    self.kc = 0.12
+    self.ff = 0.005
+    self.vca = 60
+    self.vk = -75
+    self.vl = -50.0
+    self.gk = 2.5
+    self.cm = 5
+    self.gbk = 1
+    self.gca = 2.1
+    self.gsk = 2
+    self.vm = -20
+    self.vb = -5
+    self.sn = 10
+    self.sm = 12
+    self.sbk = 2
+    self.taun = 30
+    self.taubk = 5
+    self.ks = 0.4
+    self.alpha = 0.0015
+    self.gl = 0.2
+
+    # variables
+    self.V = bm.Variable(bm.random.random(self.num) * -90 + 20)
+    self.n = bm.Variable(bm.random.random(self.num) / 2)
+    self.b = bm.Variable(bm.random.random(self.num) / 2)
+    self.c = bm.Variable(bm.random.random(self.num))
+    self.input = bm.Variable(self.num)
+
+    # integrators
+    self.integral = bp.odeint(bp.JointEq(self.dV, self.dn, self.dc, self.db), method='exp_euler')
+
+  def dn(self, n, t, V):
+    ninf = 1 / (1 + bm.exp((self.vn - V) / self.sn))
+    return (ninf - n) / self.taun
+
+  def db(self, b, t, V):
+    bkinf = 1 / (1 + bm.exp((self.vb - V) / self.sbk))
+    return (bkinf - b) / self.taubk
+
+  def dc(self, c, t, V):
+    minf = 1 / (1 + bm.exp((self.vm - V) / self.sm))
+    ica = self.gca * minf * (V - self.vca)
+    return -self.ff * (self.alpha * ica + self.kc * c)
+
+  def dV(self, V, t, n, b, c):
+    minf = 1 / (1 + bm.exp((self.vm - V) / self.sm))
+    cinf = c ** 2 / (c ** 2 + self.ks * self.ks)
+    ica = self.gca * minf * (V - self.vca)
+    isk = self.gsk * cinf * (V - self.vk)
+    ibk = self.gbk * b * (V - self.vk)
+    ikdr = self.gk * n * (V - self.vk)
+    il = self.gl * (V - self.vl)
+    return -(ica + isk + ibk + ikdr + il + self.input) / self.cm
+
+  def update(self, tdi, x=None):
+    V, n, c, b = self.integral(self.V.value, self.n.value, self.c.value, self.b.value, tdi.t, tdi.dt)
+    self.V.value = V
+    self.n.value = n
+    self.c.value = c
+    self.b.value = b
+
+  def clear_input(self):
+    self.input.value = bm.zeros_like(self.input)
+
+
+class PituitaryNetwork(bp.Network):
+  def __init__(self, num, gc):
+    super(PituitaryNetwork, self).__init__()
+
+    self.N = PituitaryCell(num)
+    self.gj = bp.synapses.GapJunction(self.N, self.N, bp.conn.All2All(include_self=False), g_max=gc)
+
+
+if __name__ == '__main__':
+  net = PituitaryNetwork(2, 0.002)
+  runner = bp.DSRunner(net, monitors={'V': net.N.V}, dt=0.5)
+  runner.run(10 * 1e3)
+
+  fig, gs = bp.visualize.get_figure(1, 1, 6, 10)
+  fig.add_subplot(gs[0, 0])
+  bp.visualize.line_plot(runner.mon.ts, runner.mon.V, plot_ids=(0, 1), show=True)

extensions/brainpylib/atomic_sum.py

@@ -115,7 +115,8 @@ def _atomic_sum_translation(c, values, pre_ids, post_ids, *, post_num, platform=
         shape_with_layout=x_shape(np.dtype(values_dtype), (post_num,), (0,)),
       )
   elif platform == 'gpu':
-    if gpu_ops is None: raise ValueError('Cannot find compiled gpu wheels.')
+    if gpu_ops is None:
+      raise ValueError('Cannot find compiled gpu wheels.')
 
     opaque = gpu_ops.build_atomic_sum_descriptor(conn_size, post_num)
     if values_dim[0] != 1:

extensions/brainpylib/event_sum.py

@@ -7,14 +7,17 @@
 
 from functools import partial
 
+from typing import Union, Tuple
 import jax.numpy as jnp
 import numpy as np
-from jax import core
+from jax import core, dtypes
 from jax.abstract_arrays import ShapedArray
 from jax.interpreters import xla, batching
 from jax.lax import scan
 from jax.lib import xla_client
 
+from .utils import GPUOperatorNotFound
+
 try:
   from . import gpu_ops
 except ImportError:
@@ -26,7 +29,10 @@
 _event_sum_prim = core.Primitive("event_sum")
 
 
-def event_sum(events, pre2post, post_num, values):
+def event_sum(events: jnp.ndarray,
+              pre2post: Tuple[jnp.ndarray, jnp.ndarray],
+              post_num: int,
+              values: Union[float, jnp.ndarray]):
   # events
   if events.dtype != jnp.bool_:
     raise ValueError(f'"events" must be a vector of bool, while we got {events.dtype}')
@@ -39,17 +45,16 @@ def event_sum(events, pre2post, post_num, values):
   if indices.dtype != indptr.dtype:
     raise ValueError(f"The dtype of pre2post[0] must be equal to that of pre2post[1], "
                      f"while we got {(indices.dtype, indptr.dtype)}")
-  if indices.dtype not in [jnp.uint32, jnp.uint64]:
-    raise ValueError(f'The dtype of pre2post must be uint32 or uint64, while we got {indices.dtype}')
+  if indices.dtype not in [jnp.uint32, jnp.uint64, jnp.int32, jnp.int64]:
+    raise ValueError(f'The dtype of pre2post must be integer, while we got {indices.dtype}')
 
   # output value
-  values = jnp.asarray([values])
-  if values.dtype not in [jnp.float32, jnp.float64]:
-    raise ValueError(f'The dtype of "values" must be float32 or float64, while we got {values.dtype}.')
-  if values.size not in [1, indices.size]:
+  dtype = values.dtype if isinstance(values, jnp.ndarray) else dtypes.canonicalize_dtype(type(values))
+  if dtype not in [jnp.float32, jnp.float64]:
+    raise ValueError(f'The dtype of "values" must be float32 or float64, while we got {dtype}.')
+  if np.size(values) not in [1, indices.size]:
     raise ValueError(f'The size of "values" must be 1 (a scalar) or len(pre2post[0]) (a vector), '
-                     f'while we got {values.size} != 1 != {indices.size}')
-  values = values.flatten()
+                     f'while we got {np.size(values)} != 1 != {indices.size}')
   # bind operator
   return _event_sum_prim.bind(events, indices, indptr, values, post_num=post_num)
 
@@ -58,34 +63,27 @@ def _event_sum_abstract(events, indices, indptr, values, *, post_num):
   return ShapedArray(dtype=values.dtype, shape=(post_num,))
 
 
-_event_sum_prim.def_abstract_eval(_event_sum_abstract)
-_event_sum_prim.def_impl(partial(xla.apply_primitive, _event_sum_prim))
-
-
 def _event_sum_translation(c, events, indices, indptr, values, *, post_num, platform="cpu"):
-  # The pre/post shape
+  # The shape of pre/post
   pre_size = np.array(c.get_shape(events).dimensions()[0], dtype=np.uint32)
   _pre_shape = x_shape(np.dtype(np.uint32), (), ())
   _post_shape = x_shape(np.dtype(np.uint32), (), ())
 
   # The indices shape
   indices_shape = c.get_shape(indices)
   Itype = indices_shape.element_type()
-  assert Itype in [np.uint32, np.uint64]
 
   # The value shape
   values_shape = c.get_shape(values)
   Ftype = values_shape.element_type()
-  assert Ftype in [np.float32, np.float64]
   values_dim = values_shape.dimensions()
 
   # We dispatch a different call depending on the dtype
-  f_type = b'_f32' if Ftype == np.float32 else b'_f64'
-  i_type = b'_i32' if Itype == np.uint32 else b'_i64'
+  f_type = b'_f32' if Ftype in np.float32 else b'_f64'
+  i_type = b'_i32' if Itype in [np.uint32, np.int32] else b'_i64'
 
-  # And then the following is what changes between the GPU and CPU
   if platform == "cpu":
-    v_type = b'_event_sum_homo' if values_dim[0] == 1 else b'_event_sum_heter'
+    v_type = b'_event_sum_homo' if len(values_dim) == 0 else b'_event_sum_heter'
     return x_ops.CustomCallWithLayout(
       c,
       platform.encode() + v_type + f_type + i_type,
@@ -103,9 +101,12 @@ def _event_sum_translation(c, events, indices, indptr, values, *, post_num, plat
                                   c.get_shape(values)),
       shape_with_layout=x_shape(np.dtype(Ftype), (post_num,), (0,)),
     )
+
+  # GPU platform
   elif platform == 'gpu':
     if gpu_ops is None:
-      raise ValueError('Cannot find compiled gpu wheels.')
+      raise GPUOperatorNotFound('event_sum')
+
     v_type = b'_event_sum_homo' if values_dim[0] == 1 else b'_event_sum_heter'
     opaque = gpu_ops.build_event_sum_descriptor(pre_size, post_num)
     return x_ops.CustomCallWithLayout(
@@ -127,11 +128,7 @@ def _event_sum_translation(c, events, indices, indptr, values, *, post_num, plat
     raise ValueError("Unsupported platform, we only support 'cpu' or 'gpu'")
 
 
-xla.backend_specific_translations["cpu"][_event_sum_prim] = partial(_event_sum_translation, platform="cpu")
-xla.backend_specific_translations["gpu"][_event_sum_prim] = partial(_event_sum_translation, platform="gpu")
-
-
-def _event_sum_batch(args, axes):
+def _event_sum_batch(args, axes, *, post_num):
   batch_axes, batch_args, non_batch_args = [], {}, {}
   for ax_i, ax in enumerate(axes):
     if ax is None:
@@ -143,19 +140,22 @@ def _event_sum_batch(args, axes):
   def f(_, x):
     pars = tuple([(x[f'ax{i}'] if i in batch_axes else non_batch_args[f'ax{i}'])
                   for i in range(len(axes))])
-    return 0, _event_sum_prim.bind(*pars)
+    return 0, _event_sum_prim.bind(*pars, post_num=post_num)
+
   _, outs = scan(f, 0, batch_args)
   return outs, 0
 
 
+_event_sum_prim.def_abstract_eval(_event_sum_abstract)
+_event_sum_prim.def_impl(partial(xla.apply_primitive, _event_sum_prim))
 batching.primitive_batchers[_event_sum_prim] = _event_sum_batch
-
+xla.backend_specific_translations["cpu"][_event_sum_prim] = partial(_event_sum_translation, platform="cpu")
+xla.backend_specific_translations["gpu"][_event_sum_prim] = partial(_event_sum_translation, platform="gpu")
 
 # ---------------------------
 # event sum kernel 2
 # ---------------------------
 
-
 _event_sum2_prim = core.Primitive("event_sum2")
 
 

extensions/brainpylib/utils.py

@@ -0,0 +1,18 @@
+# -*- coding: utf-8 -*-
+
+
+__all__ = [
+  'GPUOperatorNotFound',
+]
+
+
+class GPUOperatorNotFound(Exception):
+  def __init__(self, name):
+    super(GPUOperatorNotFound, self).__init__(f'''
+GPU operator for "{name}" does not found. 
+
+Please compile brainpylib GPU operators with the guidance in the following link:
+
+https://brainpy.readthedocs.io/en/latest/tutorial_advanced/compile_brainpylib.html
+    ''')
+