Source code for brainpy.math.operators.op_register

# -*- coding: utf-8 -*-

from typing import Callable

import brainpylib
from jax.tree_util import tree_map

from brainpy.base import Base
from brainpy.math.jaxarray import JaxArray

__all__ = [

[docs]class XLACustomOp(Base): """Creating a XLA custom call operator. 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 cpu_func: callable The function defines the computation on CPU backend. Same as ``con_compute``. gpu_func: callable The function defines the computation on GPU backend. Currently, this function is not supportted. apply_cpu_func_to_gpu: bool Whether allows to apply CPU function on GPU backend. If True, the GPU data will move to CPU, and after calculation, the returned outputs on CPU backend will move to GPU. """
[docs] def __init__( self, eval_shape: Callable = None, con_compute: Callable = None, cpu_func: Callable = None, gpu_func: Callable = None, apply_cpu_func_to_gpu: bool = False, name: str = None, batching_translation: Callable = None, jvp_translation: Callable = None, transpose_translation: Callable = None, multiple_results: bool = False, ): super(XLACustomOp, self).__init__(name=name) # abstract evaluation function if eval_shape is None: raise ValueError('Must provide "eval_shape" for abstract evaluation.') # cpu function if con_compute is None: if cpu_func is None: raise ValueError('Must provide one of "cpu_func" or "con_compute".') else: cpu_func = con_compute # gpu function if gpu_func is None: gpu_func = None # register OP self.op = brainpylib.register_op_with_numba(, cpu_func=cpu_func, gpu_func_translation=gpu_func, out_shapes=eval_shape, apply_cpu_func_to_gpu=apply_cpu_func_to_gpu, 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, JaxArray) else a, args, is_leaf=lambda a: isinstance(a, JaxArray)) kwargs = tree_map(lambda a: a.value if isinstance(a, JaxArray) else a, kwargs, is_leaf=lambda a: isinstance(a, JaxArray)) res = self.op.bind(*args, **kwargs) return res