Source code for spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_static

# Copyright (c) 2015 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Iterable, List, Optional, Tuple, TYPE_CHECKING

import numpy
from numpy import floating, integer, uint8, uint32
from numpy.typing import NDArray

from pyNN.standardmodels.synapses import StaticSynapse

from spinn_utilities.overrides import overrides

from spinn_front_end_common.interface.ds import DataSpecificationBase
from spinn_front_end_common.utilities.constants import BYTES_PER_WORD

from spynnaker.pyNN.exceptions import SynapticConfigurationException
from spynnaker.pyNN.models.neuron.synapse_dynamics.types import (
    NUMPY_CONNECTORS_DTYPE)
from spynnaker.pyNN.types import Weight_Delay_In_Types as _Weight
from spynnaker.pyNN.utilities.utility_calls import get_n_bits

from .abstract_static_synapse_dynamics import AbstractStaticSynapseDynamics
from .abstract_generate_on_machine import (
    AbstractGenerateOnMachine, MatrixGeneratorID)

from .synapse_dynamics_neuromodulation import SynapseDynamicsNeuromodulation

if TYPE_CHECKING:
    from .abstract_synapse_dynamics import AbstractSynapseDynamics
    from spynnaker.pyNN.models.neural_projections import (
        ProjectionApplicationEdge, SynapseInformation)
    from spynnaker.pyNN.models.neuron.synapse_io import MaxRowInfo
    from spynnaker.pyNN.models.neuron.synapse_dynamics.types import (
        ConnectionsArray)


class SynapseDynamicsStatic(
        AbstractStaticSynapseDynamics,
        AbstractGenerateOnMachine):
    """
    The dynamics of a synapse that does not change over time.
    """

    __slots__ = [
        # padding to add to a synaptic row for synaptic rewiring
        "__pad_to_length"]

    def __init__(
            self, weight: _Weight = StaticSynapse.default_parameters['weight'],
            delay: Optional[float] = None,
            pad_to_length: Optional[int] = None):

        """
        :param float weight:
        :param delay: Use ``None`` to get the simulator default minimum delay.
        :type delay: float or None
        :param int pad_to_length:
        """
        super(AbstractStaticSynapseDynamics, self).__init__(
            delay=delay, weight=weight)
        self.__pad_to_length = pad_to_length

[docs] @overrides(AbstractStaticSynapseDynamics.merge) def merge(self, synapse_dynamics: AbstractSynapseDynamics ) -> AbstractSynapseDynamics: # Neuromodulation shouldn't be used without STDP if isinstance(synapse_dynamics, SynapseDynamicsNeuromodulation): raise SynapticConfigurationException( "Neuromodulation can only be added when an STDP projection" " has already been added") # We can always override a static synapse dynamics with a more # complex model return synapse_dynamics
[docs] @overrides(AbstractStaticSynapseDynamics.is_same_as) def is_same_as(self, synapse_dynamics: AbstractSynapseDynamics) -> bool: return isinstance(synapse_dynamics, SynapseDynamicsStatic)
[docs] @overrides(AbstractStaticSynapseDynamics.get_vertex_executable_suffix) def get_vertex_executable_suffix(self) -> str: return ""
[docs] @overrides(AbstractStaticSynapseDynamics. get_parameters_sdram_usage_in_bytes) def get_parameters_sdram_usage_in_bytes( self, n_neurons: int, n_synapse_types: int) -> int: return 0
[docs] @overrides(AbstractStaticSynapseDynamics.write_parameters) def write_parameters( self, spec: DataSpecificationBase, region: int, global_weight_scale: float, synapse_weight_scales: NDArray[floating]): # Nothing to do here pass
[docs] @overrides( AbstractStaticSynapseDynamics.get_n_words_for_static_connections) def get_n_words_for_static_connections(self, n_connections: int) -> int: if (self.__pad_to_length is not None and n_connections < self.__pad_to_length): n_connections = self.__pad_to_length return n_connections
[docs] @overrides(AbstractStaticSynapseDynamics.get_static_synaptic_data) def get_static_synaptic_data( self, connections: ConnectionsArray, connection_row_indices: NDArray[integer], n_rows: int, n_synapse_types: int, max_n_synapses: int, max_atoms_per_core: int) -> Tuple[ List[NDArray], NDArray]: # pylint: disable=too-many-arguments n_neuron_id_bits = get_n_bits(max_atoms_per_core) neuron_id_mask = (1 << n_neuron_id_bits) - 1 n_synapse_type_bits = get_n_bits(n_synapse_types) fixed_fixed = ( ((numpy.rint(numpy.abs(connections["weight"])).astype(uint32) & 0xFFFF) << 16) | (connections["delay"].astype(uint32) << (n_neuron_id_bits + n_synapse_type_bits)) | (connections["synapse_type"].astype(uint32) << n_neuron_id_bits) | (connections["target"] & neuron_id_mask)) fixed_fixed_rows = self.convert_per_connection_data_to_rows( connection_row_indices, n_rows, fixed_fixed.view(uint8).reshape((-1, BYTES_PER_WORD)), max_n_synapses) ff_size = self.get_n_items(fixed_fixed_rows, BYTES_PER_WORD) if self.__pad_to_length is not None: # Pad the data fixed_fixed_rows = self._pad_row(fixed_fixed_rows, BYTES_PER_WORD) ff_data = [fixed_row.view(uint32) for fixed_row in fixed_fixed_rows] return ff_data, ff_size
def _pad_row(self, rows, no_bytes_per_connection): """ :param list(~numpy.ndarray) rows: :param int no_bytes_per_connection: :rtype: list(~numpy.ndarray) """ return [ numpy.concatenate([ row, numpy.zeros(numpy.clip( no_bytes_per_connection * self.__pad_to_length - row.size, 0, None)).astype(dtype=uint8)]).view(uint8) for row in rows] # Row elements are (individual) bytes
[docs] @overrides(AbstractStaticSynapseDynamics.get_n_static_words_per_row) def get_n_static_words_per_row(self, ff_size: NDArray) -> NDArray: # The sizes are in words, so just return them return ff_size
[docs] @overrides(AbstractStaticSynapseDynamics.get_n_synapses_in_rows) def get_n_synapses_in_rows(self, ff_size: NDArray) -> NDArray: # Each word is a synapse and sizes are in words, so just return them return ff_size
[docs] @overrides(AbstractStaticSynapseDynamics.read_static_synaptic_data) def read_static_synaptic_data( self, n_synapse_types: int, ff_size: NDArray[integer], ff_data: List[NDArray[uint32]], max_atoms_per_core: int) -> ConnectionsArray: n_synapse_type_bits = get_n_bits(n_synapse_types) n_neuron_id_bits = get_n_bits(max_atoms_per_core) neuron_id_mask = (1 << n_neuron_id_bits) - 1 data = numpy.concatenate(ff_data) connections = numpy.zeros(data.size, dtype=NUMPY_CONNECTORS_DTYPE) connections["source"] = numpy.concatenate( [numpy.repeat(i, ff_size[i]) for i in range(len(ff_size))]) connections["target"] = data & neuron_id_mask connections["weight"] = (data >> 16) & 0xFFFF connections["delay"] = (data & 0xFFFF) >> ( n_neuron_id_bits + n_synapse_type_bits) return connections
[docs] @overrides(AbstractStaticSynapseDynamics.get_parameter_names) def get_parameter_names(self) -> Iterable[str]: return ('weight', 'delay')
[docs] @overrides(AbstractStaticSynapseDynamics.get_max_synapses) def get_max_synapses(self, n_words: int) -> int: return n_words
@property @overrides(AbstractGenerateOnMachine.gen_matrix_id) def gen_matrix_id(self) -> int: return MatrixGeneratorID.STATIC_MATRIX.value
[docs] @overrides(AbstractGenerateOnMachine.gen_matrix_params) def gen_matrix_params( self, synaptic_matrix_offset: int, delayed_matrix_offset: int, app_edge: ProjectionApplicationEdge, synapse_info: SynapseInformation, max_row_info: MaxRowInfo, max_pre_atoms_per_core: int, max_post_atoms_per_core: int) -> NDArray[uint32]: vertex = app_edge.post_vertex n_synapse_type_bits = get_n_bits( vertex.neuron_impl.get_n_synapse_types()) n_synapse_index_bits = get_n_bits(max_post_atoms_per_core) max_delay = vertex.splitter.max_support_delay() max_delay_bits = get_n_bits(max_delay) return numpy.array([ synaptic_matrix_offset, delayed_matrix_offset, max_row_info.undelayed_max_words, max_row_info.delayed_max_words, synapse_info.synapse_type, n_synapse_type_bits, n_synapse_index_bits, app_edge.n_delay_stages + 1, max_delay, max_delay_bits, app_edge.pre_vertex.n_atoms, max_pre_atoms_per_core], dtype=uint32)
@property @overrides(AbstractGenerateOnMachine. gen_matrix_params_size_in_bytes) def gen_matrix_params_size_in_bytes(self) -> int: return 12 * BYTES_PER_WORD @property @overrides(AbstractStaticSynapseDynamics.changes_during_run) def changes_during_run(self) -> bool: return False @property @overrides(AbstractStaticSynapseDynamics.pad_to_length) def pad_to_length(self) -> Optional[int]: return self.__pad_to_length @property @overrides(AbstractStaticSynapseDynamics.is_combined_core_capable) def is_combined_core_capable(self) -> bool: return True