[doc] update operator customization

brainpy/_src/math/ndarray.py

@@ -1181,7 +1181,7 @@ def addr_(
       *,
       beta: float = 1.0,
       alpha: float = 1.0
-  ) -> None:
+  ):
     vec1 = _as_jax_array_(vec1)
     vec2 = _as_jax_array_(vec2)
     r = alpha * jnp.outer(vec1, vec2) + beta * self.value
@@ -1217,7 +1217,7 @@ def absolute(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = Non
     """
     return self.abs(out=out)
 
-  def absolute_(self) -> None:
+  def absolute_(self):
     """
     alias of Array.abs_()
     """
@@ -1258,11 +1258,11 @@ def sin(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None) ->
       _check_out(out)
       out.value = r
 
-  def sin_(self) -> None:
+  def sin_(self):
     self.value = jnp.sin(self.value)
     return self
 
-  def cos_(self) -> None:
+  def cos_(self):
     self.value = jnp.cos(self.value)
     return self
 
@@ -1274,7 +1274,7 @@ def cos(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None) ->
       _check_out(out)
       out.value = r
 
-  def tan_(self) -> None:
+  def tan_(self):
     self.value = jnp.tan(self.value)
     return self
 
@@ -1286,7 +1286,7 @@ def tan(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None) ->
       _check_out(out)
       out.value = r
 
-  def sinh_(self) -> None:
+  def sinh_(self):
     self.value = jnp.tanh(self.value)
     return self
 
@@ -1298,7 +1298,7 @@ def sinh(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None) -
       _check_out(out)
       out.value = r
 
-  def cosh_(self) -> None:
+  def cosh_(self):
     self.value = jnp.cosh(self.value)
     return self
 
@@ -1310,7 +1310,7 @@ def cosh(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None) -
       _check_out(out)
       out.value = r
 
-  def tanh_(self) -> None:
+  def tanh_(self):
     self.value = jnp.tanh(self.value)
     return self
 
@@ -1322,7 +1322,7 @@ def tanh(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None) -
       _check_out(out)
       out.value = r
 
-  def arcsin_(self) -> None:
+  def arcsin_(self):
     self.value = jnp.arcsin(self.value)
     return self
 
@@ -1334,7 +1334,7 @@ def arcsin(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None)
       _check_out(out)
       out.value = r
 
-  def arccos_(self) -> None:
+  def arccos_(self):
     self.value = jnp.arccos(self.value)
     return self
 
@@ -1346,7 +1346,7 @@ def arccos(self, *, out: Optional[Union['Array', jax.Array, np.ndarray]] = None)
       _check_out(out)
       out.value = r
 
-  def arctan_(self) -> None:
+  def arctan_(self):
     self.value = jnp.arctan(self.value)
     return self
 
@@ -1381,7 +1381,7 @@ def clamp(
 
   def clamp_(self,
              min_value: Optional[Union['Array', jax.Array, np.ndarray]] = None,
-             max_value: Optional[Union['Array', jax.Array, np.ndarray]] = None) -> None:
+             max_value: Optional[Union['Array', jax.Array, np.ndarray]] = None):
     """
     return the value between min_value and max_value,
     if min_value is None, then no lower bound,
@@ -1392,7 +1392,7 @@ def clamp_(self,
 
   def clip_(self,
             min_value: Optional[Union['Array', jax.Array, np.ndarray]] = None,
-            max_value: Optional[Union['Array', jax.Array, np.ndarray]] = None) -> None:
+            max_value: Optional[Union['Array', jax.Array, np.ndarray]] = None):
     """
     alias for clamp_
     """
@@ -1402,7 +1402,7 @@ def clip_(self,
   def clone(self) -> 'Array':
     return Array(self.value.copy())
 
-  def copy_(self, src: Union['Array', jax.Array, np.ndarray]) -> None:
+  def copy_(self, src: Union['Array', jax.Array, np.ndarray]) -> 'Array':
     self.value = jnp.copy(_as_jax_array_(src))
     return self
 
@@ -1507,6 +1507,16 @@ def cpu(self):
     self.value = jax.device_put(self.value, jax.devices('cpu')[0])
     return self
 
+  # dtype exchanging #
+  # ---------------- #
+
+  def bool(self): return jnp.asarray(self.value, dtypt=jnp.bool_)
+  def int(self): return jnp.asarray(self.value, dtypt=jnp.int32)
+  def long(self): return jnp.asarray(self.value, dtypt=jnp.int64)
+  def half(self): return jnp.asarray(self.value, dtypt=jnp.float16)
+  def float(self): return jnp.asarray(self.value, dtypt=jnp.float32)
+  def double(self): return jnp.asarray(self.value, dtype=jnp.float64)
+
 
 JaxArray = Array
 ndarray = Array

brainpy/_src/math/op_registers/__init__.py

@@ -1,5 +1,6 @@
 
 from .numba_approach import (XLACustomOp,
+                             CustomOpByNumba,
                              register_op_with_numba,
                              compile_cpu_signature_with_numba)
 from .utils import register_general_batching

brainpy/_src/math/op_registers/numba_approach/__init__.py

@@ -16,12 +16,74 @@
 from .cpu_translation import _cpu_translation, compile_cpu_signature_with_numba
 
 __all__ = [
+  'CustomOpByNumba',
   'XLACustomOp',
   'register_op_with_numba',
   'compile_cpu_signature_with_numba',
 ]
 
 
+class CustomOpByNumba(BrainPyObject):
+  """Creating a XLA custom call operator with Numba JIT on CPU backend.
+
+  Parameters
+  ----------
+  name: str
+    The name of operator.
+  eval_shape: callable
+    The function to evaluate the shape and dtype of the output according to the input.
+    This function should receive the abstract information of inputs, and return the
+    abstract information of the outputs. For example:
+
+    >>> def eval_shape(inp1_info, inp2_info, inp3_info, ...):
+    >>>   return out1_info, out2_info
+  con_compute: callable
+    The function to make the concrete computation. This function receives inputs,
+    and returns outputs. For example:
+
+    >>> def con_compute(inp1, inp2, inp3, ...):
+    >>>   return out1, out2
+  """
+
+  def __init__(
+      self,
+      eval_shape: Callable = None,
+      con_compute: Callable = None,
+      name: str = None,
+      batching_translation: Callable = None,
+      jvp_translation: Callable = None,
+      transpose_translation: Callable = None,
+      multiple_results: bool = True,
+  ):
+    super().__init__(name=name)
+
+    # abstract evaluation function
+    if eval_shape is None:
+      raise ValueError('Must provide "eval_shape" for abstract evaluation.')
+
+    # cpu function
+    cpu_func = con_compute
+
+    # register OP
+    self.op = register_op_with_numba(
+      self.name,
+      cpu_func=cpu_func,
+      out_shapes=eval_shape,
+      batching_translation=batching_translation,
+      jvp_translation=jvp_translation,
+      transpose_translation=transpose_translation,
+      multiple_results=multiple_results,
+    )
+
+  def __call__(self, *args, **kwargs):
+    args = tree_map(lambda a: a.value if isinstance(a, Array) else a,
+                    args, is_leaf=lambda a: isinstance(a, Array))
+    kwargs = tree_map(lambda a: a.value if isinstance(a, Array) else a,
+                      kwargs, is_leaf=lambda a: isinstance(a, Array))
+    res = self.op.bind(*args, **kwargs)
+    return res
+
+
 class XLACustomOp(BrainPyObject):
   """Creating a XLA custom call operator.
 
@@ -175,8 +237,9 @@ def abs_eval_rule(*input_shapes, **info):
       shapes = out_shapes
 
     if isinstance(shapes, core.ShapedArray):
-      pass
+      assert not multiple_results, "multiple_results is True, while the abstract evaluation returns only one data."
     elif isinstance(shapes, (tuple, list)):
+      assert multiple_results, "multiple_results is False, while the abstract evaluation returns multiple data."
       for elem in shapes:
         if not isinstance(elem, core.ShapedArray):
           raise ValueError(f'Elements in "out_shapes" must be instances of '

brainpy/_src/math/surrogate/_one_input.py

@@ -37,7 +37,7 @@ def __init__(self, forward_use_surrogate=False):
     self.forward_use_surrogate = forward_use_surrogate
     self._true_call_ = jax.custom_gradient(self.call)
 
-  def __call__(self, x: Union[jax.Array, Array]):
+  def __call__(self, x: jax.Array):
     return self._true_call_(as_jax(x))
 
   def call(self, x):
@@ -70,7 +70,7 @@ class Sigmoid(_OneInpSurrogate):
 
   """
 
-  def __init__(self, alpha=4., forward_use_surrogate=False):
+  def __init__(self, alpha: float = 4., forward_use_surrogate=False):
     super().__init__(forward_use_surrogate)
     self.alpha = alpha
 
@@ -154,7 +154,7 @@ class PiecewiseQuadratic(_OneInpSurrogate):
 
   """
 
-  def __init__(self, alpha=1., forward_use_surrogate=False):
+  def __init__(self, alpha: float = 1., forward_use_surrogate=False):
     super().__init__(forward_use_surrogate)
     self.alpha = alpha
 
@@ -258,7 +258,7 @@ class PiecewiseExp(_OneInpSurrogate):
   piecewise_exp
   """
 
-  def __init__(self, alpha=1., forward_use_surrogate=False):
+  def __init__(self, alpha: float = 1., forward_use_surrogate=False):
     super().__init__(forward_use_surrogate)
     self.alpha = alpha
 

brainpy/math/op_register.py

@@ -2,6 +2,7 @@
 
 
 from brainpy._src.math.op_registers import (
+  CustomOpByNumba,
   XLACustomOp,
   compile_cpu_signature_with_numba,
 )

docs/apis/brainpy.math.op_register.rst

@@ -0,0 +1,29 @@
+Operator Registration
+=====================
+
+.. contents::
+   :local:
+   :depth: 1
+
+
+CPU Operator Customization with Numba
+-------------------------------------
+
+.. currentmodule:: brainpy.math
+.. automodule:: brainpy.math
+
+.. autosummary::
+   :toctree: generated/
+   :nosignatures:
+   :template: classtemplate.rst
+
+   CustomOpByNumba
+   XLACustomOp
+
+
+.. autosummary::
+   :toctree: generated/
+
+   register_op_with_numba
+   compile_cpu_signature_with_numba
+

docs/apis/math.rst

@@ -35,4 +35,5 @@ dynamics programming. For more information and usage examples, please refer to t
    brainpy.math.sharding.rst
    brainpy.math.environment.rst
    brainpy.math.modes.rst
+   brainpy.math.op_register.rst
 

docs/tutorial_advanced/3_dedicated_operators.rst

@@ -4,4 +4,4 @@ Brain Dynamics Dedicated Operators
 .. toctree::
    :maxdepth: 1
 
-   low-level_operator_customization.ipynb
+   operator_custom_with_numba.ipynb