class AbstractPopulationVertex(
ApplicationVertex, AbstractGeneratesDataSpecification,
AbstractHasAssociatedBinary, AbstractContainsUnits,
AbstractSpikeRecordable, AbstractNeuronRecordable,
AbstractProvidesOutgoingPartitionConstraints,
AbstractProvidesIncomingPartitionConstraints,
AbstractPopulationInitializable, AbstractPopulationSettable,
AbstractChangableAfterRun, AbstractHasGlobalMaxAtoms,
AbstractRewritesDataSpecification, AbstractReadParametersBeforeSet,
AbstractAcceptsIncomingSynapses, ProvidesKeyToAtomMappingImpl):
""" Underlying vertex model for Neural Populations.
"""
__slots__ = [
"_additional_input", "_binary", "_buffer_size_before_receive",
"_change_requires_mapping",
"_change_requires_neuron_parameters_reload",
"_incoming_spike_buffer_size", "_input_type",
"_maximum_sdram_for_buffering", "_minimum_buffer_sdram", "_model_name",
"_n_atoms", "_n_profile_samples", "_neuron_model", "_neuron_recorder",
"_receive_buffer_host", "_receive_buffer_port", "_spike_recorder",
"_synapse_manager", "_threshold_type", "_time_between_requests",
"_units", "_using_auto_pause_and_resume"
]
BASIC_MALLOC_USAGE = 2
# recording region ids
SPIKE_RECORDING_REGION = 0
V_RECORDING_REGION = 1
GSYN_EXCITATORY_RECORDING_REGION = 2
GSYN_INHIBITORY_RECORDING_REGION = 3
RECORDING_REGION = {"spikes": 0, "v": 1, "gsyn_exc": 2, "gsyn_inh": 3}
VARIABLE_LONG = {
"spikes": "spikes",
"v": "membrane voltage",
"gsyn_exc": "gsyn_excitatory",
"gsyn_inh": "gsyn_inhibitory"
}
N_RECORDING_REGIONS = 4
# the size of the runtime SDP port data region
RUNTIME_SDP_PORT_SIZE = 4
# 6 elements before the start of global parameters
BYTES_TILL_START_OF_GLOBAL_PARAMETERS = 24
_n_vertices = 0
non_pynn_default_parameters = {
'spikes_per_second': None,
'ring_buffer_sigma': None,
'incoming_spike_buffer_size': None,
'constraints': None,
'label': None
}
def __init__(self,
n_neurons,
binary,
label,
max_atoms_per_core,
spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
model_name,
neuron_model,
input_type,
synapse_type,
threshold_type,
additional_input=None,
constraints=None):
# pylint: disable=too-many-arguments, too-many-locals
super(AbstractPopulationVertex, self).__init__(label, constraints,
max_atoms_per_core)
self._units = {
'spikes': 'spikes',
'v': 'mV',
'gsyn_exc': "uS",
'gsyn_inh': "uS"
}
self._binary = binary
self._n_atoms = n_neurons
# buffer data
self._incoming_spike_buffer_size = incoming_spike_buffer_size
# get config from simulator
config = globals_variables.get_simulator().config
if incoming_spike_buffer_size is None:
self._incoming_spike_buffer_size = config.getint(
"Simulation", "incoming_spike_buffer_size")
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._neuron_recorder = NeuronRecorder(
["spikes", "v", "gsyn_exc", "gsyn_inh"], n_neurons)
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = helpful_functions.read_config_int(
config, "Buffers", "receive_buffer_port")
# If live buffering is enabled, set a maximum on the buffer sizes
spike_buffer_max_size = 0
v_buffer_max_size = 0
gsyn_buffer_max_size = 0
self._buffer_size_before_receive = None
if config.getboolean("Buffers", "enable_buffered_recording"):
spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
v_buffer_max_size = config.getint("Buffers", "v_buffer_size")
gsyn_buffer_max_size = config.getint("Buffers", "gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._maximum_sdram_for_buffering = [
spike_buffer_max_size, v_buffer_max_size, gsyn_buffer_max_size,
gsyn_buffer_max_size
]
# Set up synapse handling
self._synapse_manager = SynapticManager(synapse_type,
ring_buffer_sigma,
spikes_per_second, config)
# bool for if state has changed.
self._change_requires_mapping = True
self._change_requires_neuron_parameters_reload = False
# Set up for profiling
self._n_profile_samples = helpful_functions.read_config_int(
config, "Reports", "n_profile_samples")
@property
@overrides(ApplicationVertex.n_atoms)
def n_atoms(self):
return self._n_atoms
@inject_items({
"graph": "MemoryApplicationGraph",
"n_machine_time_steps": "TotalMachineTimeSteps",
"machine_time_step": "MachineTimeStep"
})
@overrides(ApplicationVertex.get_resources_used_by_atoms,
additional_arguments={
"graph", "n_machine_time_steps", "machine_time_step"
})
def get_resources_used_by_atoms(self, vertex_slice, graph,
n_machine_time_steps, machine_time_step):
# pylint: disable=arguments-differ
# set resources required from this object
container = ResourceContainer(
sdram=SDRAMResource(
self.get_sdram_usage_for_atoms(vertex_slice, graph,
machine_time_step)),
dtcm=DTCMResource(self.get_dtcm_usage_for_atoms(vertex_slice)),
cpu_cycles=CPUCyclesPerTickResource(
self.get_cpu_usage_for_atoms(vertex_slice)))
recording_sizes = recording_utilities.get_recording_region_sizes(
self._get_buffered_sdram(vertex_slice, n_machine_time_steps),
self._minimum_buffer_sdram, self._maximum_sdram_for_buffering,
self._using_auto_pause_and_resume)
container.extend(
recording_utilities.get_recording_resources(
recording_sizes, self._receive_buffer_host,
self._receive_buffer_port))
# return the total resources.
return container
@property
@overrides(AbstractChangableAfterRun.requires_mapping)
def requires_mapping(self):
return self._change_requires_mapping
@overrides(AbstractChangableAfterRun.mark_no_changes)
def mark_no_changes(self):
self._change_requires_mapping = False
def _get_buffered_sdram_per_timestep(self, vertex_slice):
return [
self._neuron_recorder.get_buffered_sdram_per_timestep(
"spikes", vertex_slice),
self._neuron_recorder.get_buffered_sdram_per_timestep(
"v", vertex_slice),
self._neuron_recorder.get_buffered_sdram_per_timestep(
"gsyn_exc", vertex_slice),
self._neuron_recorder.get_buffered_sdram_per_timestep(
"gsyn_inh", vertex_slice)
]
def _get_buffered_sdram(self, vertex_slice, n_machine_time_steps):
return [
self._neuron_recorder.get_buffered_sdram("spikes", vertex_slice,
n_machine_time_steps),
self._neuron_recorder.get_buffered_sdram("v", vertex_slice,
n_machine_time_steps),
self._neuron_recorder.get_buffered_sdram("gsyn_exc", vertex_slice,
n_machine_time_steps),
self._neuron_recorder.get_buffered_sdram("gsyn_inh", vertex_slice,
n_machine_time_steps)
]
@inject_items({"n_machine_time_steps": "TotalMachineTimeSteps"})
@overrides(ApplicationVertex.create_machine_vertex,
additional_arguments={"n_machine_time_steps"})
def create_machine_vertex(self,
vertex_slice,
resources_required,
n_machine_time_steps,
label=None,
constraints=None):
# pylint: disable=too-many-arguments, arguments-differ
is_recording = len(self._neuron_recorder.recording_variables) > 0
buffered_sdram_per_timestep = self._get_buffered_sdram_per_timestep(
vertex_slice)
buffered_sdram = self._get_buffered_sdram(vertex_slice,
n_machine_time_steps)
minimum_buffer_sdram = recording_utilities.get_minimum_buffer_sdram(
buffered_sdram, self._minimum_buffer_sdram)
overflow_sdram = self._neuron_recorder.get_sampling_overflow_sdram(
vertex_slice)
vertex = PopulationMachineVertex(resources_required, is_recording,
minimum_buffer_sdram,
buffered_sdram_per_timestep, label,
constraints, overflow_sdram)
AbstractPopulationVertex._n_vertices += 1
# return machine vertex
return vertex
def get_cpu_usage_for_atoms(self, vertex_slice):
per_neuron_cycles = (
_NEURON_BASE_N_CPU_CYCLES_PER_NEURON +
self._neuron_model.get_n_cpu_cycles_per_neuron() +
self._input_type.get_n_cpu_cycles_per_neuron(
self._synapse_manager.synapse_type.get_n_synapse_types()) +
self._threshold_type.get_n_cpu_cycles_per_neuron())
if self._additional_input is not None:
per_neuron_cycles += \
self._additional_input.get_n_cpu_cycles_per_neuron()
return (_NEURON_BASE_N_CPU_CYCLES + _C_MAIN_BASE_N_CPU_CYCLES +
(per_neuron_cycles * vertex_slice.n_atoms) +
self._neuron_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._synapse_manager.get_n_cpu_cycles())
def get_dtcm_usage_for_atoms(self, vertex_slice):
per_neuron_usage = (
self._neuron_model.get_dtcm_usage_per_neuron_in_bytes() +
self._input_type.get_dtcm_usage_per_neuron_in_bytes() +
self._threshold_type.get_dtcm_usage_per_neuron_in_bytes())
if self._additional_input is not None:
per_neuron_usage += \
self._additional_input.get_dtcm_usage_per_neuron_in_bytes()
return (_NEURON_BASE_DTCM_USAGE_IN_BYTES +
(per_neuron_usage * vertex_slice.n_atoms) +
self._neuron_recorder.get_dtcm_usage_in_bytes(vertex_slice) +
self._synapse_manager.get_dtcm_usage_in_bytes())
def _get_sdram_usage_for_neuron_params_per_neuron(self):
per_neuron_usage = (
self._input_type.get_sdram_usage_per_neuron_in_bytes() +
self._threshold_type.get_sdram_usage_per_neuron_in_bytes() +
self._neuron_recorder.get_sdram_usage_per_neuron_in_bytes() +
self._neuron_model.get_sdram_usage_per_neuron_in_bytes())
if self._additional_input is not None:
per_neuron_usage += \
self._additional_input.get_sdram_usage_per_neuron_in_bytes()
return per_neuron_usage
def _get_sdram_usage_for_neuron_params(self, vertex_slice):
""" calculates the sdram usage for just the neuron parameters region
:param vertex_slice: the slice of atoms.
:return: The sdram required for the neuron region
"""
per_neuron_usage = \
self._get_sdram_usage_for_neuron_params_per_neuron()
return (self.BYTES_TILL_START_OF_GLOBAL_PARAMETERS +
self._neuron_model.
get_sdram_usage_for_global_parameters_in_bytes() +
self._neuron_recorder.
get_sdram_usage_for_global_parameters_in_bytes() +
(per_neuron_usage * vertex_slice.n_atoms))
def get_sdram_usage_for_atoms(self, vertex_slice, graph,
machine_time_step):
sdram_requirement = (
common_constants.SYSTEM_BYTES_REQUIREMENT +
self._get_sdram_usage_for_neuron_params(vertex_slice) +
recording_utilities.get_recording_header_size(
self.N_RECORDING_REGIONS) +
PopulationMachineVertex.get_provenance_data_size(
PopulationMachineVertex.N_ADDITIONAL_PROVENANCE_DATA_ITEMS) +
self._synapse_manager.get_sdram_usage_in_bytes(
vertex_slice, graph.get_edges_ending_at_vertex(self),
machine_time_step) +
(self._get_number_of_mallocs_used_by_dsg() *
common_constants.SARK_PER_MALLOC_SDRAM_USAGE) +
profile_utils.get_profile_region_size(self._n_profile_samples))
return sdram_requirement
def _get_number_of_mallocs_used_by_dsg(self):
extra_mallocs = len(self._neuron_recorder.recording_variables)
return (self.BASIC_MALLOC_USAGE +
self._synapse_manager.get_number_of_mallocs_used_by_dsg() +
extra_mallocs)
def _reserve_memory_regions(self, spec, vertex_slice, vertex):
spec.comment("\nReserving memory space for data regions:\n\n")
# Reserve memory:
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.SYSTEM.value,
size=common_constants.SYSTEM_BYTES_REQUIREMENT,
label='System')
self._reserve_neuron_params_data_region(spec, vertex_slice)
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.RECORDING.value,
size=recording_utilities.get_recording_header_size(
self.N_RECORDING_REGIONS))
profile_utils.reserve_profile_region(
spec, constants.POPULATION_BASED_REGIONS.PROFILING.value,
self._n_profile_samples)
vertex.reserve_provenance_data_region(spec)
def _reserve_neuron_params_data_region(self, spec, vertex_slice):
""" reserve the neuron parameter data region
:param spec: the spec to write the dsg region to
:param vertex_slice: the slice of atoms from the application vertex
:return: None
"""
params_size = self._get_sdram_usage_for_neuron_params(vertex_slice)
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value,
size=params_size,
label='NeuronParams')
def _write_neuron_parameters(self, spec, key, vertex_slice,
machine_time_step, time_scale_factor):
# pylint: disable=too-many-arguments
n_atoms = (vertex_slice.hi_atom - vertex_slice.lo_atom) + 1
spec.comment(
"\nWriting Neuron Parameters for {} Neurons:\n".format(n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write the random back off value
spec.write_value(
random.randint(0, AbstractPopulationVertex._n_vertices))
# Write the number of microseconds between sending spikes
time_between_spikes = ((machine_time_step * time_scale_factor) /
(n_atoms * 2.0))
spec.write_value(data=int(time_between_spikes))
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the size of the incoming spike buffer
spec.write_value(data=self._incoming_spike_buffer_size)
# Write the recording rates and sizes
record_globals = self._neuron_recorder.get_global_parameters(
vertex_slice)
for param in record_globals:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the index parameters
indexes = self._neuron_recorder.get_index_parameters(vertex_slice)
utility_calls.write_parameters_per_neuron(spec,
vertex_slice,
indexes,
slice_paramaters=True)
# Write the global parameters
global_params = self._neuron_model.get_global_parameters()
for param in global_params:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the neuron parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._neuron_model.get_neural_parameters())
# Write the input type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._input_type.get_input_type_parameters())
# Write the additional input parameters
if self._additional_input is not None:
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._additional_input.get_parameters())
# Write the threshold type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._threshold_type.get_threshold_parameters())
@inject_items({
"machine_time_step": "MachineTimeStep",
"time_scale_factor": "TimeScaleFactor",
"graph_mapper": "MemoryGraphMapper",
"routing_info": "MemoryRoutingInfos"
})
@overrides(AbstractRewritesDataSpecification.regenerate_data_specification,
additional_arguments={
"machine_time_step", "time_scale_factor", "graph_mapper",
"routing_info"
})
def regenerate_data_specification(self, spec, placement, machine_time_step,
time_scale_factor, graph_mapper,
routing_info):
# pylint: disable=too-many-arguments, arguments-differ
vertex_slice = graph_mapper.get_slice(placement.vertex)
# reserve the neuron parameters data region
self._reserve_neuron_params_data_region(
spec, graph_mapper.get_slice(placement.vertex))
# write the neuron params into the new dsg region
self._write_neuron_parameters(
key=routing_info.get_first_key_from_pre_vertex(
placement.vertex, constants.SPIKE_PARTITION_ID),
machine_time_step=machine_time_step,
spec=spec,
time_scale_factor=time_scale_factor,
vertex_slice=vertex_slice)
self._synapse_manager.regenerate_data_specification(
spec, placement, machine_time_step, time_scale_factor,
vertex_slice)
# close spec
spec.end_specification()
@overrides(AbstractRewritesDataSpecification.
requires_memory_regions_to_be_reloaded)
def requires_memory_regions_to_be_reloaded(self):
return self._change_requires_neuron_parameters_reload
@overrides(AbstractRewritesDataSpecification.mark_regions_reloaded)
def mark_regions_reloaded(self):
self._change_requires_neuron_parameters_reload = False
@inject_items({
"machine_time_step": "MachineTimeStep",
"time_scale_factor": "TimeScaleFactor",
"graph_mapper": "MemoryGraphMapper",
"application_graph": "MemoryApplicationGraph",
"machine_graph": "MemoryMachineGraph",
"routing_info": "MemoryRoutingInfos",
"tags": "MemoryTags",
"n_machine_time_steps": "TotalMachineTimeSteps"
})
@overrides(AbstractGeneratesDataSpecification.generate_data_specification,
additional_arguments={
"machine_time_step", "time_scale_factor", "graph_mapper",
"application_graph", "machine_graph", "routing_info",
"tags", "n_machine_time_steps"
})
def generate_data_specification(self, spec, placement, machine_time_step,
time_scale_factor, graph_mapper,
application_graph, machine_graph,
routing_info, tags, n_machine_time_steps):
# pylint: disable=too-many-arguments, arguments-differ
vertex = placement.vertex
spec.comment("\n*** Spec for block of {} neurons ***\n".format(
self._model_name))
vertex_slice = graph_mapper.get_slice(vertex)
# Reserve memory regions
self._reserve_memory_regions(spec, vertex_slice, vertex)
# Declare random number generators and distributions:
# TODO add random distribution stuff
# self.write_random_distribution_declarations(spec)
# Get the key
key = routing_info.get_first_key_from_pre_vertex(
vertex, constants.SPIKE_PARTITION_ID)
# Write the setup region
spec.switch_write_focus(
constants.POPULATION_BASED_REGIONS.SYSTEM.value)
spec.write_array(
simulation_utilities.get_simulation_header_array(
self.get_binary_file_name(), machine_time_step,
time_scale_factor))
# Write the recording region
spec.switch_write_focus(
constants.POPULATION_BASED_REGIONS.RECORDING.value)
ip_tags = tags.get_ip_tags_for_vertex(vertex)
recorded_region_sizes = recording_utilities.get_recorded_region_sizes(
self._get_buffered_sdram(vertex_slice, n_machine_time_steps),
self._maximum_sdram_for_buffering)
spec.write_array(
recording_utilities.get_recording_header_array(
recorded_region_sizes, self._time_between_requests,
self._buffer_size_before_receive, ip_tags))
# Write the neuron parameters
self._write_neuron_parameters(spec, key, vertex_slice,
machine_time_step, time_scale_factor)
# write profile data
profile_utils.write_profile_region_data(
spec, constants.POPULATION_BASED_REGIONS.PROFILING.value,
self._n_profile_samples)
# allow the synaptic matrix to write its data spec-able data
self._synapse_manager.write_data_spec(spec, self, vertex_slice, vertex,
placement, machine_graph,
application_graph, routing_info,
graph_mapper, self._input_type,
machine_time_step)
# End the writing of this specification:
spec.end_specification()
@overrides(AbstractHasAssociatedBinary.get_binary_file_name)
def get_binary_file_name(self):
# Split binary name into title and extension
binary_title, binary_extension = os.path.splitext(self._binary)
# Reunite title and extension and return
return (binary_title + self._synapse_manager.vertex_executable_suffix +
binary_extension)
@overrides(AbstractHasAssociatedBinary.get_binary_start_type)
def get_binary_start_type(self):
return ExecutableType.USES_SIMULATION_INTERFACE
@overrides(AbstractSpikeRecordable.is_recording_spikes)
def is_recording_spikes(self):
return self._neuron_recorder.is_recording("spikes")
@overrides(AbstractSpikeRecordable.set_recording_spikes)
def set_recording_spikes(self,
new_state=True,
sampling_interval=None,
indexes=None):
self.set_recording("spikes", new_state, sampling_interval, indexes)
@overrides(AbstractSpikeRecordable.get_spikes)
def get_spikes(self, placements, graph_mapper, buffer_manager,
machine_time_step):
return self._neuron_recorder.get_spikes(self.label, buffer_manager,
self.SPIKE_RECORDING_REGION,
placements, graph_mapper, self,
machine_time_step)
@overrides(AbstractNeuronRecordable.get_recordable_variables)
def get_recordable_variables(self):
return self._neuron_recorder.get_recordable_variables()
@overrides(AbstractNeuronRecordable.is_recording)
def is_recording(self, variable):
return self._neuron_recorder.is_recording(variable)
@overrides(AbstractNeuronRecordable.set_recording)
def set_recording(self,
variable,
new_state=True,
sampling_interval=None,
indexes=None):
self._change_requires_mapping = not self.is_recording(variable)
self._neuron_recorder.set_recording(variable, new_state,
sampling_interval, indexes)
@overrides(AbstractNeuronRecordable.get_data)
def get_data(self, variable, n_machine_time_steps, placements,
graph_mapper, buffer_manager, machine_time_step):
# pylint: disable=too-many-arguments
return self._neuron_recorder.get_matrix_data(
self.label, buffer_manager, self.RECORDING_REGION[variable],
placements, graph_mapper, self, variable, n_machine_time_steps)
@overrides(AbstractNeuronRecordable.get_neuron_sampling_interval)
def get_neuron_sampling_interval(self, variable):
return self._neuron_recorder.get_neuron_sampling_interval(variable)
@overrides(AbstractSpikeRecordable.get_spikes_sampling_interval)
def get_spikes_sampling_interval(self):
return self._neuron_recorder.get_neuron_sampling_interval("spikes")
@overrides(AbstractPopulationInitializable.initialize)
def initialize(self, variable, value):
initialize_attr = getattr(self._neuron_model,
"initialize_%s" % variable, None)
if initialize_attr is None or not callable(initialize_attr):
raise Exception("Vertex does not support initialisation of"
" parameter {}".format(variable))
initialize_attr(value)
self._change_requires_neuron_parameters_reload = True
@property
def input_type(self):
return self._input_type
@overrides(AbstractPopulationSettable.get_value)
def get_value(self, key):
""" Get a property of the overall model
"""
for obj in [
self._neuron_model, self._input_type, self._threshold_type,
self._synapse_manager.synapse_type, self._additional_input,
self
]:
if hasattr(obj, key):
return getattr(obj, key)
raise Exception("Population {} does not have parameter {}".format(
self._model_name, key))
@overrides(AbstractPopulationSettable.set_value)
def set_value(self, key, value):
""" Set a property of the overall model
"""
for obj in [
self._neuron_model, self._input_type, self._threshold_type,
self._synapse_manager.synapse_type, self._additional_input
]:
if hasattr(obj, key):
setattr(obj, key, value)
self._change_requires_neuron_parameters_reload = True
return
raise InvalidParameterType("Type {} does not have parameter {}".format(
type(self), key))
@overrides(AbstractReadParametersBeforeSet.read_parameters_from_machine)
def read_parameters_from_machine(self, transceiver, placement,
vertex_slice):
# locate sdram address to where the neuron parameters are stored
neuron_region_sdram_address = \
helpful_functions.locate_memory_region_for_placement(
placement,
constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value,
transceiver)
# shift past the extra stuff before neuron parameters that we don't
# need to read
neuron_parameters_sdram_address = (
neuron_region_sdram_address +
self.BYTES_TILL_START_OF_GLOBAL_PARAMETERS)
# get size of neuron params
size_of_region = self._get_sdram_usage_for_neuron_params(vertex_slice)
size_of_region -= self.BYTES_TILL_START_OF_GLOBAL_PARAMETERS
# get data from the machine
byte_array = transceiver.read_memory(placement.x, placement.y,
neuron_parameters_sdram_address,
size_of_region)
# Skip the recorder globals as these are not change on machione
# Just written out in case data is changed and written back
offset = self._neuron_recorder.get_size_of_global_parameters(
vertex_slice)
# update python neuron parameters with the data
# handle global params (only once, so given a slice of 0 to 0)
global_params, offset = utility_calls.translate_parameters(
self._neuron_model.get_global_parameter_types(), byte_array,
offset, Slice(0, 0))
self._neuron_model.set_global_parameters(global_params)
# handle state params for a neuron
neuron_params, offset = utility_calls.translate_parameters(
self._neuron_model.get_neural_parameter_types(), byte_array,
offset, vertex_slice)
self._neuron_model.set_neural_parameters(neuron_params, vertex_slice)
# handle input params
input_params, offset = utility_calls.translate_parameters(
self._input_type.get_input_type_parameter_types(), byte_array,
offset, vertex_slice)
self._input_type.set_input_type_parameters(input_params, vertex_slice)
# handle additional input params, if they exist
if self._additional_input is not None:
additional_params, offset = utility_calls.translate_parameters(
self._additional_input.get_parameter_types(), byte_array,
offset, vertex_slice)
self._additional_input.set_parameters(additional_params,
vertex_slice)
# handle threshold type params
threshold_params, offset = utility_calls.translate_parameters(
self._threshold_type.get_threshold_parameter_types(), byte_array,
offset, vertex_slice)
self._threshold_type.set_threshold_parameters(threshold_params,
vertex_slice)
# Read synapse parameters
self._synapse_manager.read_parameters_from_machine(
transceiver, placement, vertex_slice)
@property
def weight_scale(self):
return self._input_type.get_global_weight_scale()
@property
def ring_buffer_sigma(self):
return self._synapse_manager.ring_buffer_sigma
@ring_buffer_sigma.setter
def ring_buffer_sigma(self, ring_buffer_sigma):
self._synapse_manager.ring_buffer_sigma = ring_buffer_sigma
@property
def spikes_per_second(self):
return self._synapse_manager.spikes_per_second
@spikes_per_second.setter
def spikes_per_second(self, spikes_per_second):
self._synapse_manager.spikes_per_second = spikes_per_second
@property
def synapse_dynamics(self):
return self._synapse_manager.synapse_dynamics
def set_synapse_dynamics(self, synapse_dynamics):
self._synapse_manager.synapse_dynamics = synapse_dynamics
def add_pre_run_connection_holder(self, connection_holder, edge,
synapse_info):
# pylint: disable=arguments-differ
self._synapse_manager.add_pre_run_connection_holder(
connection_holder, edge, synapse_info)
@overrides(AbstractAcceptsIncomingSynapses.get_connections_from_machine)
def get_connections_from_machine(
self,
transceiver,
placement,
edge,
graph_mapper,
routing_infos,
synapse_information,
machine_time_step,
using_extra_monitor_cores,
placements=None,
data_receiver=None,
sender_extra_monitor_core_placement=None,
extra_monitor_cores_for_router_timeout=None,
handle_time_out_configuration=True,
fixed_routes=None):
# pylint: disable=too-many-arguments
return self._synapse_manager.get_connections_from_machine(
transceiver, placement, edge, graph_mapper, routing_infos,
synapse_information, machine_time_step, using_extra_monitor_cores,
placements, data_receiver, sender_extra_monitor_core_placement,
extra_monitor_cores_for_router_timeout,
handle_time_out_configuration, fixed_routes)
def clear_connection_cache(self):
self._synapse_manager.clear_connection_cache()
@property
def synapse_type(self):
return self._synapse_manager.synapse_type
def get_maximum_delay_supported_in_ms(self, machine_time_step):
return self._synapse_manager.get_maximum_delay_supported_in_ms(
machine_time_step)
@overrides(AbstractProvidesIncomingPartitionConstraints.
get_incoming_partition_constraints)
def get_incoming_partition_constraints(self, partition):
""" Gets the constraints for partitions going into this vertex
:param partition: partition that goes into this vertex
:return: list of constraints
"""
return self._synapse_manager.get_incoming_partition_constraints()
@overrides(AbstractProvidesOutgoingPartitionConstraints.
get_outgoing_partition_constraints)
def get_outgoing_partition_constraints(self, partition):
""" Gets the constraints for partitions going out of this vertex
:param partition: the partition that leaves this vertex
:return: list of constraints
"""
return [ContiguousKeyRangeContraint()]
@overrides(AbstractNeuronRecordable.clear_recording)
def clear_recording(self, variable, buffer_manager, placements,
graph_mapper):
self._clear_recording_region(buffer_manager, placements, graph_mapper,
self.RECORDING_REGION[variable])
@overrides(AbstractSpikeRecordable.clear_spike_recording)
def clear_spike_recording(self, buffer_manager, placements, graph_mapper):
self._clear_recording_region(
buffer_manager, placements, graph_mapper,
AbstractPopulationVertex.SPIKE_RECORDING_REGION)
def _clear_recording_region(self, buffer_manager, placements, graph_mapper,
recording_region_id):
""" clears a recorded data region from the buffer manager
:param buffer_manager: the buffer manager object
:param placements: the placements object
:param graph_mapper: the graph mapper object
:param recording_region_id: the recorded region id for clearing
:rtype: None
"""
machine_vertices = graph_mapper.get_machine_vertices(self)
for machine_vertex in machine_vertices:
placement = placements.get_placement_of_vertex(machine_vertex)
buffer_manager.clear_recorded_data(placement.x, placement.y,
placement.p,
recording_region_id)
@overrides(AbstractContainsUnits.get_units)
def get_units(self, variable):
# search the model components for the variable
for obj in [
self._neuron_model, self._input_type, self._threshold_type,
self._synapse_manager.synapse_type, self._additional_input
]:
if (hasattr(obj, variable)
and isinstance(obj, AbstractContainsUnits)):
return obj.unit(variable)
# if not in the components, must be within myself, so call my own units
if variable in self._units:
return self._units[variable]
else:
raise InvalidParameterType(
"The parameter {} does not exist in this input "
"conductance component".format(variable))
def describe(self):
"""
Returns a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template
together with an associated template engine
(see ``pyNN.descriptions``).
If template is None, then a dictionary containing the template context
will be returned.
"""
parameters = dict()
for parameter_name in self.default_parameters:
parameters[parameter_name] = self.get_value(parameter_name)
context = {
"name": self._model_name,
"default_parameters": self.default_parameters,
"default_initial_values": self.default_parameters,
"parameters": parameters,
}
return context
def __str__(self):
return "{} with {} atoms".format(self.label, self.n_atoms)
def __repr__(self):
return self.__str__()
class AbstractPopulationVertex(
ApplicationVertex, AbstractGeneratesDataSpecification,
AbstractHasAssociatedBinary, AbstractContainsUnits,
AbstractSpikeRecordable, AbstractNeuronRecordable,
AbstractProvidesOutgoingPartitionConstraints,
AbstractProvidesIncomingPartitionConstraints,
AbstractPopulationInitializable, AbstractPopulationSettable,
AbstractChangableAfterRun, AbstractRewritesDataSpecification,
AbstractReadParametersBeforeSet, AbstractAcceptsIncomingSynapses,
ProvidesKeyToAtomMappingImpl):
""" Underlying vertex model for Neural Populations.
"""
__slots__ = [
"_buffer_size_before_receive", "_change_requires_mapping",
"_change_requires_neuron_parameters_reload",
"_incoming_spike_buffer_size", "_maximum_sdram_for_buffering",
"_minimum_buffer_sdram", "_n_atoms", "_n_profile_samples",
"_neuron_impl", "_neuron_recorder", "_parameters", "_pynn_model",
"_receive_buffer_host", "_receive_buffer_port", "_state_variables",
"_synapse_manager", "_time_between_requests", "_units",
"_using_auto_pause_and_resume"
]
BASIC_MALLOC_USAGE = 2
# recording region IDs
SPIKE_RECORDING_REGION = 0
# the size of the runtime SDP port data region
RUNTIME_SDP_PORT_SIZE = 4
# 8 elements before the start of global parameters
BYTES_TILL_START_OF_GLOBAL_PARAMETERS = 32
_n_vertices = 0
def __init__(self, n_neurons, label, constraints, max_atoms_per_core,
spikes_per_second, ring_buffer_sigma,
incoming_spike_buffer_size, neuron_impl, pynn_model):
# pylint: disable=too-many-arguments, too-many-locals
super(AbstractPopulationVertex, self).__init__(label, constraints,
max_atoms_per_core)
self._n_atoms = n_neurons
# buffer data
self._incoming_spike_buffer_size = incoming_spike_buffer_size
# get config from simulator
config = globals_variables.get_simulator().config
if incoming_spike_buffer_size is None:
self._incoming_spike_buffer_size = config.getint(
"Simulation", "incoming_spike_buffer_size")
self._neuron_impl = neuron_impl
self._pynn_model = pynn_model
self._parameters = SpynnakerRangeDictionary(n_neurons)
self._state_variables = SpynnakerRangeDictionary(n_neurons)
self._neuron_impl.add_parameters(self._parameters)
self._neuron_impl.add_state_variables(self._state_variables)
# Set up for recording
recordables = ["spikes"]
recordables.extend(self._neuron_impl.get_recordable_variables())
self._neuron_recorder = NeuronRecorder(recordables, n_neurons)
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = helpful_functions.read_config_int(
config, "Buffers", "receive_buffer_port")
# If live buffering is enabled, set a maximum on the buffer sizes
spike_buffer_max_size = 0
variable_buffer_max_size = 0
self._buffer_size_before_receive = None
if config.getboolean("Buffers", "enable_buffered_recording"):
spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
variable_buffer_max_size = config.getint("Buffers",
"variable_buffer_size")
self._maximum_sdram_for_buffering = [spike_buffer_max_size]
for _ in self._neuron_impl.get_recordable_variables():
self._maximum_sdram_for_buffering.append(variable_buffer_max_size)
# Set up synapse handling
self._synapse_manager = SynapticManager(
self._neuron_impl.get_n_synapse_types(), ring_buffer_sigma,
spikes_per_second, config)
# bool for if state has changed.
self._change_requires_mapping = True
self._change_requires_neuron_parameters_reload = False
# Set up for profiling
self._n_profile_samples = helpful_functions.read_config_int(
config, "Reports", "n_profile_samples")
@property
@overrides(ApplicationVertex.n_atoms)
def n_atoms(self):
return self._n_atoms
@inject_items({
"graph": "MemoryApplicationGraph",
"n_machine_time_steps": "TotalMachineTimeSteps",
"machine_time_step": "MachineTimeStep"
})
@overrides(ApplicationVertex.get_resources_used_by_atoms,
additional_arguments={
"graph", "n_machine_time_steps", "machine_time_step"
})
def get_resources_used_by_atoms(self, vertex_slice, graph,
n_machine_time_steps, machine_time_step):
# pylint: disable=arguments-differ
# set resources required from this object
container = ResourceContainer(
sdram=SDRAMResource(
self.get_sdram_usage_for_atoms(vertex_slice, graph,
machine_time_step)),
dtcm=DTCMResource(self.get_dtcm_usage_for_atoms(vertex_slice)),
cpu_cycles=CPUCyclesPerTickResource(
self.get_cpu_usage_for_atoms(vertex_slice)))
recording_sizes = recording_utilities.get_recording_region_sizes(
self._get_buffered_sdram(vertex_slice, n_machine_time_steps),
self._minimum_buffer_sdram, self._maximum_sdram_for_buffering,
self._using_auto_pause_and_resume)
container.extend(
recording_utilities.get_recording_resources(
recording_sizes, self._receive_buffer_host,
self._receive_buffer_port))
# return the total resources.
return container
@property
@overrides(AbstractChangableAfterRun.requires_mapping)
def requires_mapping(self):
return self._change_requires_mapping
@overrides(AbstractChangableAfterRun.mark_no_changes)
def mark_no_changes(self):
self._change_requires_mapping = False
def _get_buffered_sdram_per_timestep(self, vertex_slice):
values = [
self._neuron_recorder.get_buffered_sdram_per_timestep(
"spikes", vertex_slice)
]
for variable in self._neuron_impl.get_recordable_variables():
values.append(
self._neuron_recorder.get_buffered_sdram_per_timestep(
variable, vertex_slice))
return values
def _get_buffered_sdram(self, vertex_slice, n_machine_time_steps):
values = [
self._neuron_recorder.get_buffered_sdram("spikes", vertex_slice,
n_machine_time_steps)
]
for variable in self._neuron_impl.get_recordable_variables():
values.append(
self._neuron_recorder.get_buffered_sdram(
variable, vertex_slice, n_machine_time_steps))
return values
@inject_items({"n_machine_time_steps": "TotalMachineTimeSteps"})
@overrides(ApplicationVertex.create_machine_vertex,
additional_arguments={"n_machine_time_steps"})
def create_machine_vertex(self,
vertex_slice,
resources_required,
n_machine_time_steps,
label=None,
constraints=None):
# pylint: disable=too-many-arguments, arguments-differ
is_recording = len(self._neuron_recorder.recording_variables) > 0
buffered_sdram_per_timestep = self._get_buffered_sdram_per_timestep(
vertex_slice)
buffered_sdram = self._get_buffered_sdram(vertex_slice,
n_machine_time_steps)
minimum_buffer_sdram = recording_utilities.get_minimum_buffer_sdram(
buffered_sdram, self._minimum_buffer_sdram)
overflow_sdram = self._neuron_recorder.get_sampling_overflow_sdram(
vertex_slice)
vertex = PopulationMachineVertex(resources_required, is_recording,
minimum_buffer_sdram,
buffered_sdram_per_timestep, label,
constraints, overflow_sdram)
AbstractPopulationVertex._n_vertices += 1
# return machine vertex
return vertex
def get_cpu_usage_for_atoms(self, vertex_slice):
return (_NEURON_BASE_N_CPU_CYCLES + _C_MAIN_BASE_N_CPU_CYCLES +
(_NEURON_BASE_N_CPU_CYCLES_PER_NEURON * vertex_slice.n_atoms) +
self._neuron_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._neuron_impl.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._synapse_manager.get_n_cpu_cycles())
def get_dtcm_usage_for_atoms(self, vertex_slice):
return (
_NEURON_BASE_DTCM_USAGE_IN_BYTES +
self._neuron_impl.get_dtcm_usage_in_bytes(vertex_slice.n_atoms) +
self._neuron_recorder.get_dtcm_usage_in_bytes(vertex_slice) +
self._synapse_manager.get_dtcm_usage_in_bytes())
def _get_sdram_usage_for_neuron_params(self, vertex_slice):
""" Calculate the SDRAM usage for just the neuron parameters region.
:param vertex_slice: the slice of atoms.
:return: The SDRAM required for the neuron region
"""
return (
self.BYTES_TILL_START_OF_GLOBAL_PARAMETERS +
self._neuron_recorder.get_sdram_usage_in_bytes(vertex_slice) +
self._neuron_impl.get_sdram_usage_in_bytes(vertex_slice.n_atoms))
def get_sdram_usage_for_atoms(self, vertex_slice, graph,
machine_time_step):
sdram_requirement = (
common_constants.SYSTEM_BYTES_REQUIREMENT +
self._get_sdram_usage_for_neuron_params(vertex_slice) +
recording_utilities.get_recording_header_size(
len(self._neuron_impl.get_recordable_variables()) + 1) +
PopulationMachineVertex.get_provenance_data_size(
PopulationMachineVertex.N_ADDITIONAL_PROVENANCE_DATA_ITEMS) +
self._synapse_manager.get_sdram_usage_in_bytes(
vertex_slice, graph.get_edges_ending_at_vertex(self),
machine_time_step) +
(self._get_number_of_mallocs_used_by_dsg() *
common_constants.SARK_PER_MALLOC_SDRAM_USAGE) +
profile_utils.get_profile_region_size(self._n_profile_samples))
return sdram_requirement
def _get_number_of_mallocs_used_by_dsg(self):
extra_mallocs = len(self._neuron_recorder.recording_variables)
return (self.BASIC_MALLOC_USAGE +
self._synapse_manager.get_number_of_mallocs_used_by_dsg() +
extra_mallocs)
def _reserve_memory_regions(self, spec, vertex_slice, vertex):
spec.comment("\nReserving memory space for data regions:\n\n")
# Reserve memory:
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.SYSTEM.value,
size=common_constants.SYSTEM_BYTES_REQUIREMENT,
label='System')
self._reserve_neuron_params_data_region(spec, vertex_slice)
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.RECORDING.value,
size=recording_utilities.get_recording_header_size(
len(self._neuron_impl.get_recordable_variables()) + 1))
profile_utils.reserve_profile_region(
spec, constants.POPULATION_BASED_REGIONS.PROFILING.value,
self._n_profile_samples)
vertex.reserve_provenance_data_region(spec)
def _reserve_neuron_params_data_region(self, spec, vertex_slice):
""" Reserve the neuron parameter data region.
:param spec: the spec to write the DSG region to
:param vertex_slice: the slice of atoms from the application vertex
:return: None
"""
params_size = self._get_sdram_usage_for_neuron_params(vertex_slice)
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value,
size=params_size,
label='NeuronParams')
def _write_neuron_parameters(self, spec, key, vertex_slice,
machine_time_step, time_scale_factor):
# pylint: disable=too-many-arguments
n_atoms = vertex_slice.n_atoms
spec.comment(
"\nWriting Neuron Parameters for {} Neurons:\n".format(n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write the random back off value
spec.write_value(
random.randint(0, AbstractPopulationVertex._n_vertices))
# Write the number of microseconds between sending spikes
time_between_spikes = ((machine_time_step * time_scale_factor) /
(n_atoms * 2.0))
spec.write_value(data=int(time_between_spikes))
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the number of synapse types
spec.write_value(data=self._neuron_impl.get_n_synapse_types())
# Write the size of the incoming spike buffer
spec.write_value(data=self._incoming_spike_buffer_size)
# Write the number of variables that can be recorded
spec.write_value(
data=len(self._neuron_impl.get_recordable_variables()))
# Write the recording data
recording_data = self._neuron_recorder.get_data(vertex_slice)
spec.write_array(recording_data)
# Write the neuron parameters
neuron_data = self._neuron_impl.get_data(self._parameters,
self._state_variables,
vertex_slice)
spec.write_array(neuron_data)
@inject_items({
"machine_time_step": "MachineTimeStep",
"time_scale_factor": "TimeScaleFactor",
"graph_mapper": "MemoryGraphMapper",
"routing_info": "MemoryRoutingInfos"
})
@overrides(AbstractRewritesDataSpecification.regenerate_data_specification,
additional_arguments={
"machine_time_step", "time_scale_factor", "graph_mapper",
"routing_info"
})
def regenerate_data_specification(self, spec, placement, machine_time_step,
time_scale_factor, graph_mapper,
routing_info):
# pylint: disable=too-many-arguments, arguments-differ
vertex_slice = graph_mapper.get_slice(placement.vertex)
# reserve the neuron parameters data region
self._reserve_neuron_params_data_region(
spec, graph_mapper.get_slice(placement.vertex))
# write the neuron params into the new DSG region
self._write_neuron_parameters(
key=routing_info.get_first_key_from_pre_vertex(
placement.vertex, constants.SPIKE_PARTITION_ID),
machine_time_step=machine_time_step,
spec=spec,
time_scale_factor=time_scale_factor,
vertex_slice=vertex_slice)
# close spec
spec.end_specification()
@overrides(AbstractRewritesDataSpecification.
requires_memory_regions_to_be_reloaded)
def requires_memory_regions_to_be_reloaded(self):
return self._change_requires_neuron_parameters_reload
@overrides(AbstractRewritesDataSpecification.mark_regions_reloaded)
def mark_regions_reloaded(self):
self._change_requires_neuron_parameters_reload = False
@inject_items({
"machine_time_step": "MachineTimeStep",
"time_scale_factor": "TimeScaleFactor",
"graph_mapper": "MemoryGraphMapper",
"application_graph": "MemoryApplicationGraph",
"machine_graph": "MemoryMachineGraph",
"routing_info": "MemoryRoutingInfos",
"tags": "MemoryTags",
"n_machine_time_steps": "TotalMachineTimeSteps",
"placements": "MemoryPlacements",
})
@overrides(AbstractGeneratesDataSpecification.generate_data_specification,
additional_arguments={
"machine_time_step",
"time_scale_factor",
"graph_mapper",
"application_graph",
"machine_graph",
"routing_info",
"tags",
"n_machine_time_steps",
"placements",
})
def generate_data_specification(self, spec, placement, machine_time_step,
time_scale_factor, graph_mapper,
application_graph, machine_graph,
routing_info, tags, n_machine_time_steps,
placements):
# pylint: disable=too-many-arguments, arguments-differ
vertex = placement.vertex
spec.comment("\n*** Spec for block of {} neurons ***\n".format(
self._neuron_impl.model_name))
vertex_slice = graph_mapper.get_slice(vertex)
# Reserve memory regions
self._reserve_memory_regions(spec, vertex_slice, vertex)
# Declare random number generators and distributions:
# TODO add random distribution stuff
# self.write_random_distribution_declarations(spec)
# Get the key
key = routing_info.get_first_key_from_pre_vertex(
vertex, constants.SPIKE_PARTITION_ID)
# Write the setup region
spec.switch_write_focus(
constants.POPULATION_BASED_REGIONS.SYSTEM.value)
spec.write_array(
simulation_utilities.get_simulation_header_array(
self.get_binary_file_name(), machine_time_step,
time_scale_factor))
# Write the recording region
spec.switch_write_focus(
constants.POPULATION_BASED_REGIONS.RECORDING.value)
ip_tags = tags.get_ip_tags_for_vertex(vertex)
recorded_region_sizes = recording_utilities.get_recorded_region_sizes(
self._get_buffered_sdram(vertex_slice, n_machine_time_steps),
self._maximum_sdram_for_buffering)
spec.write_array(
recording_utilities.get_recording_header_array(
recorded_region_sizes, self._time_between_requests,
self._buffer_size_before_receive, ip_tags))
# Write the neuron parameters
self._write_neuron_parameters(spec, key, vertex_slice,
machine_time_step, time_scale_factor)
# write profile data
profile_utils.write_profile_region_data(
spec, constants.POPULATION_BASED_REGIONS.PROFILING.value,
self._n_profile_samples)
# Get the weight_scale value from the appropriate location
weight_scale = self._neuron_impl.get_global_weight_scale()
# allow the synaptic matrix to write its data spec-able data
self._synapse_manager.write_data_spec(spec, self, vertex_slice, vertex,
placement, machine_graph,
application_graph, routing_info,
graph_mapper, weight_scale,
machine_time_step, placements)
# End the writing of this specification:
spec.end_specification()
@overrides(AbstractHasAssociatedBinary.get_binary_file_name)
def get_binary_file_name(self):
# Split binary name into title and extension
binary_title, binary_extension = os.path.splitext(
self._neuron_impl.binary_name)
# Reunite title and extension and return
return (binary_title + self._synapse_manager.vertex_executable_suffix +
binary_extension)
@overrides(AbstractHasAssociatedBinary.get_binary_start_type)
def get_binary_start_type(self):
return ExecutableType.USES_SIMULATION_INTERFACE
@overrides(AbstractSpikeRecordable.is_recording_spikes)
def is_recording_spikes(self):
return self._neuron_recorder.is_recording("spikes")
@overrides(AbstractSpikeRecordable.set_recording_spikes)
def set_recording_spikes(self,
new_state=True,
sampling_interval=None,
indexes=None):
self.set_recording("spikes", new_state, sampling_interval, indexes)
@overrides(AbstractSpikeRecordable.get_spikes)
def get_spikes(self, placements, graph_mapper, buffer_manager,
machine_time_step):
return self._neuron_recorder.get_spikes(self.label, buffer_manager,
self.SPIKE_RECORDING_REGION,
placements, graph_mapper, self,
machine_time_step)
@overrides(AbstractNeuronRecordable.get_recordable_variables)
def get_recordable_variables(self):
return self._neuron_recorder.get_recordable_variables()
@overrides(AbstractNeuronRecordable.is_recording)
def is_recording(self, variable):
return self._neuron_recorder.is_recording(variable)
@overrides(AbstractNeuronRecordable.set_recording)
def set_recording(self,
variable,
new_state=True,
sampling_interval=None,
indexes=None):
self._change_requires_mapping = not self.is_recording(variable)
self._neuron_recorder.set_recording(variable, new_state,
sampling_interval, indexes)
@overrides(AbstractNeuronRecordable.get_data)
def get_data(self, variable, n_machine_time_steps, placements,
graph_mapper, buffer_manager, machine_time_step):
# pylint: disable=too-many-arguments
index = 0
if variable != "spikes":
index = 1 + self._neuron_impl.get_recordable_variable_index(
variable)
return self._neuron_recorder.get_matrix_data(self.label,
buffer_manager, index,
placements, graph_mapper,
self, variable,
n_machine_time_steps)
@overrides(AbstractNeuronRecordable.get_neuron_sampling_interval)
def get_neuron_sampling_interval(self, variable):
return self._neuron_recorder.get_neuron_sampling_interval(variable)
@overrides(AbstractSpikeRecordable.get_spikes_sampling_interval)
def get_spikes_sampling_interval(self):
return self._neuron_recorder.get_neuron_sampling_interval("spikes")
@overrides(AbstractPopulationInitializable.initialize)
def initialize(self, variable, value):
if variable not in self._state_variables:
raise KeyError("Vertex does not support initialisation of"
" parameter {}".format(variable))
self._state_variables.set_value(variable, value)
self._change_requires_neuron_parameters_reload = True
@property
def initialize_parameters(self):
return self._pynn_model.default_initial_values.keys()
def _get_parameter(self, variable):
if variable.endswith("_init"):
# method called with "V_init"
key = variable[:-5]
if variable in self._state_variables:
# variable is v and parameter is v_init
return variable
elif key in self._state_variables:
# Oops neuron defines v and not v_init
return key
else:
# method called with "v"
if variable + "_init" in self._state_variables:
# variable is v and parameter is v_init
return variable + "_init"
if variable in self._state_variables:
# Oops neuron defines v and not v_init
return variable
# parameter not found for this variable
raise KeyError("No variable {} found in {}".format(
variable, self._neuron_impl.model_name))
@overrides(AbstractPopulationInitializable.get_initial_value)
def get_initial_value(self, variable, selector=None):
parameter = self._get_parameter(variable)
ranged_list = self._state_variables[parameter]
if selector is None:
return ranged_list
return ranged_list.get_values(selector)
@overrides(AbstractPopulationInitializable.set_initial_value)
def set_initial_value(self, variable, value, selector=None):
parameter = self._get_parameter(variable)
ranged_list = self._state_variables[parameter]
ranged_list.set_value_by_selector(selector, value)
@property
def conductance_based(self):
return self._neuron_impl.is_conductance_based
@overrides(AbstractPopulationSettable.get_value)
def get_value(self, key):
""" Get a property of the overall model.
"""
if key not in self._parameters:
raise InvalidParameterType(
"Population {} does not have parameter {}".format(
self._neuron_impl.model_name, key))
return self._parameters[key]
@overrides(AbstractPopulationSettable.set_value)
def set_value(self, key, value):
""" Set a property of the overall model.
"""
if key not in self._parameters:
raise InvalidParameterType(
"Population {} does not have parameter {}".format(
self._neuron_impl.model_name, key))
self._parameters.set_value(key, value)
self._change_requires_neuron_parameters_reload = True
@overrides(AbstractReadParametersBeforeSet.read_parameters_from_machine)
def read_parameters_from_machine(self, transceiver, placement,
vertex_slice):
# locate SDRAM address to where the neuron parameters are stored
neuron_region_sdram_address = \
helpful_functions.locate_memory_region_for_placement(
placement,
constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value,
transceiver)
# shift past the extra stuff before neuron parameters that we don't
# need to read
neuron_parameters_sdram_address = (
neuron_region_sdram_address +
self.BYTES_TILL_START_OF_GLOBAL_PARAMETERS)
# get size of neuron params
size_of_region = self._get_sdram_usage_for_neuron_params(vertex_slice)
size_of_region -= self.BYTES_TILL_START_OF_GLOBAL_PARAMETERS
# get data from the machine
byte_array = transceiver.read_memory(placement.x, placement.y,
neuron_parameters_sdram_address,
size_of_region)
# Skip the recorder globals as these are not change on machine
# Just written out in case data is changed and written back
offset = self._neuron_recorder.get_sdram_usage_in_bytes(vertex_slice)
# update python neuron parameters with the data
self._neuron_impl.read_data(byte_array, offset, vertex_slice,
self._parameters, self._state_variables)
@property
def weight_scale(self):
return self._neuron_impl.get_global_weight_scale()
@property
def ring_buffer_sigma(self):
return self._synapse_manager.ring_buffer_sigma
@ring_buffer_sigma.setter
def ring_buffer_sigma(self, ring_buffer_sigma):
self._synapse_manager.ring_buffer_sigma = ring_buffer_sigma
@property
def spikes_per_second(self):
return self._synapse_manager.spikes_per_second
@spikes_per_second.setter
def spikes_per_second(self, spikes_per_second):
self._synapse_manager.spikes_per_second = spikes_per_second
@property
def synapse_dynamics(self):
return self._synapse_manager.synapse_dynamics
def set_synapse_dynamics(self, synapse_dynamics):
self._synapse_manager.synapse_dynamics = synapse_dynamics
def add_pre_run_connection_holder(self, connection_holder, edge,
synapse_info):
# pylint: disable=arguments-differ
self._synapse_manager.add_pre_run_connection_holder(
connection_holder, edge, synapse_info)
@overrides(AbstractAcceptsIncomingSynapses.get_connections_from_machine)
def get_connections_from_machine(
self,
transceiver,
placement,
edge,
graph_mapper,
routing_infos,
synapse_information,
machine_time_step,
using_extra_monitor_cores,
placements=None,
data_receiver=None,
sender_extra_monitor_core_placement=None,
extra_monitor_cores_for_router_timeout=None,
handle_time_out_configuration=True,
fixed_routes=None):
# pylint: disable=too-many-arguments
return self._synapse_manager.get_connections_from_machine(
transceiver, placement, edge, graph_mapper, routing_infos,
synapse_information, machine_time_step, using_extra_monitor_cores,
placements, data_receiver, sender_extra_monitor_core_placement,
extra_monitor_cores_for_router_timeout,
handle_time_out_configuration, fixed_routes)
def clear_connection_cache(self):
self._synapse_manager.clear_connection_cache()
def get_maximum_delay_supported_in_ms(self, machine_time_step):
return self._synapse_manager.get_maximum_delay_supported_in_ms(
machine_time_step)
@overrides(AbstractProvidesIncomingPartitionConstraints.
get_incoming_partition_constraints)
def get_incoming_partition_constraints(self, partition):
""" Gets the constraints for partitions going into this vertex.
:param partition: partition that goes into this vertex
:return: list of constraints
"""
return self._synapse_manager.get_incoming_partition_constraints()
@overrides(AbstractProvidesOutgoingPartitionConstraints.
get_outgoing_partition_constraints)
def get_outgoing_partition_constraints(self, partition):
""" Gets the constraints for partitions going out of this vertex.
:param partition: the partition that leaves this vertex
:return: list of constraints
"""
return [ContiguousKeyRangeContraint()]
@overrides(AbstractNeuronRecordable.clear_recording)
def clear_recording(self, variable, buffer_manager, placements,
graph_mapper):
index = 0
if variable != "spikes":
index = 1 + self._neuron_impl.get_recordable_variable_index(
variable)
self._clear_recording_region(buffer_manager, placements, graph_mapper,
index)
@overrides(AbstractSpikeRecordable.clear_spike_recording)
def clear_spike_recording(self, buffer_manager, placements, graph_mapper):
self._clear_recording_region(
buffer_manager, placements, graph_mapper,
AbstractPopulationVertex.SPIKE_RECORDING_REGION)
def _clear_recording_region(self, buffer_manager, placements, graph_mapper,
recording_region_id):
""" Clear a recorded data region from the buffer manager.
:param buffer_manager: the buffer manager object
:param placements: the placements object
:param graph_mapper: the graph mapper object
:param recording_region_id: the recorded region ID for clearing
:rtype: None
"""
machine_vertices = graph_mapper.get_machine_vertices(self)
for machine_vertex in machine_vertices:
placement = placements.get_placement_of_vertex(machine_vertex)
buffer_manager.clear_recorded_data(placement.x, placement.y,
placement.p,
recording_region_id)
@overrides(AbstractContainsUnits.get_units)
def get_units(self, variable):
if self._neuron_impl.is_recordable(variable):
return self._neuron_impl.get_recordable_units(variable)
if variable not in self._parameters:
raise Exception("Population {} does not have parameter {}".format(
self._neuron_impl.model_name, variable))
return self._neuron_impl.get_units(variable)
def describe(self):
""" Get a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template\
together with an associated template engine\
(see ``pyNN.descriptions``).
If template is None, then a dictionary containing the template context\
will be returned.
"""
parameters = dict()
for parameter_name in self._pynn_model.default_parameters:
parameters[parameter_name] = self.get_value(parameter_name)
context = {
"name": self._neuron_impl.model_name,
"default_parameters": self._pynn_model.default_parameters,
"default_initial_values": self._pynn_model.default_parameters,
"parameters": parameters,
}
return context
def get_synapse_id_by_target(self, target):
return self._neuron_impl.get_synapse_id_by_target(target)
def __str__(self):
return "{} with {} atoms".format(self.label, self.n_atoms)
def __repr__(self):
return self.__str__()
def gen_on_machine(self, vertex_slice):
return self._synapse_manager.gen_on_machine(vertex_slice)
class AbstractPopulationVertex(
AbstractPartitionableVertex, AbstractDataSpecableVertex,
AbstractSpikeRecordable, AbstractVRecordable, AbstractGSynRecordable,
AbstractProvidesOutgoingEdgeConstraints,
AbstractProvidesIncomingEdgeConstraints,
AbstractPopulationInitializable, AbstractPopulationSettable,
AbstractMappable, ReceiveBuffersToHostBasicImpl):
""" Underlying vertex model for Neural Populations.
"""
def __init__(
self, n_neurons, binary, label, max_atoms_per_core,
machine_time_step, timescale_factor, spikes_per_second,
ring_buffer_sigma, model_name, neuron_model, input_type,
synapse_type, threshold_type, additional_input=None,
constraints=None):
ReceiveBuffersToHostBasicImpl.__init__(self)
AbstractPartitionableVertex.__init__(
self, n_neurons, label, max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step, timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
AbstractProvidesIncomingEdgeConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractMappable.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._v_buffer_max_size = config.getint(
"Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint(
"Buffers", "gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
# Set up synapse handling
self._synapse_manager = SynapticManager(
synapse_type, machine_time_step, ring_buffer_sigma,
spikes_per_second)
# Get buffering information for later use
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# bool for if state has changed.
self._change_requires_mapping = True
@property
def requires_mapping(self):
return self._change_requires_mapping
def mark_no_changes(self):
self._change_requires_mapping = False
def create_subvertex(self, vertex_slice, resources_required, label=None,
constraints=None):
return PopulationPartitionedVertex(
self.buffering_output(), resources_required, label, constraints)
# @implements AbstractPopulationVertex.get_cpu_usage_for_atoms
def get_cpu_usage_for_atoms(self, vertex_slice, graph):
per_neuron_cycles = (
_NEURON_BASE_N_CPU_CYCLES_PER_NEURON +
self._neuron_model.get_n_cpu_cycles_per_neuron() +
self._input_type.get_n_cpu_cycles_per_neuron(
self._synapse_manager.synapse_type.get_n_synapse_types()) +
self._threshold_type.get_n_cpu_cycles_per_neuron())
if self._additional_input is not None:
per_neuron_cycles += \
self._additional_input.get_n_cpu_cycles_per_neuron()
return (_NEURON_BASE_N_CPU_CYCLES +
_C_MAIN_BASE_N_CPU_CYCLES +
(per_neuron_cycles * vertex_slice.n_atoms) +
self._spike_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._v_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._gsyn_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._synapse_manager.get_n_cpu_cycles(vertex_slice, graph))
# @implements AbstractPopulationVertex.get_dtcm_usage_for_atoms
def get_dtcm_usage_for_atoms(self, vertex_slice, graph):
per_neuron_usage = (
self._neuron_model.get_dtcm_usage_per_neuron_in_bytes() +
self._input_type.get_dtcm_usage_per_neuron_in_bytes() +
self._threshold_type.get_dtcm_usage_per_neuron_in_bytes())
if self._additional_input is not None:
per_neuron_usage += \
self._additional_input.get_dtcm_usage_per_neuron_in_bytes()
return (_NEURON_BASE_DTCM_USAGE_IN_BYTES +
(per_neuron_usage * vertex_slice.n_atoms) +
self._spike_recorder.get_dtcm_usage_in_bytes() +
self._v_recorder.get_dtcm_usage_in_bytes() +
self._gsyn_recorder.get_dtcm_usage_in_bytes() +
self._synapse_manager.get_dtcm_usage_in_bytes(
vertex_slice, graph))
def _get_sdram_usage_for_neuron_params(self, vertex_slice):
per_neuron_usage = (
self._input_type.get_sdram_usage_per_neuron_in_bytes() +
self._threshold_type.get_sdram_usage_per_neuron_in_bytes())
if self._additional_input is not None:
per_neuron_usage += \
self._additional_input.get_sdram_usage_per_neuron_in_bytes()
return ((constants.DATA_SPECABLE_BASIC_SETUP_INFO_N_WORDS * 4) +
self.get_recording_data_size(3) +
(per_neuron_usage * vertex_slice.n_atoms) +
self._neuron_model.get_sdram_usage_in_bytes(
vertex_slice.n_atoms))
# @implements AbstractPopulationVertex.get_sdram_usage_for_atoms
def get_sdram_usage_for_atoms(self, vertex_slice, graph):
return (self._get_sdram_usage_for_neuron_params(vertex_slice) +
self.get_buffer_state_region_size(3) +
min((self._spike_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps),
self._spike_buffer_max_size)) +
min((self._v_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps),
self._v_buffer_max_size)) +
min((self._gsyn_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps),
self._gsyn_buffer_max_size)) +
self._synapse_manager.get_sdram_usage_in_bytes(
vertex_slice, graph.incoming_edges_to_vertex(self)) +
(self._get_number_of_mallocs_used_by_dsg(
vertex_slice, graph.incoming_edges_to_vertex(self)) *
front_end_common_constants.SARK_PER_MALLOC_SDRAM_USAGE))
# @implements AbstractPopulationVertex.model_name
def model_name(self):
return self._model_name
def _get_number_of_mallocs_used_by_dsg(self, vertex_slice, in_edges):
extra_mallocs = 0
if self._gsyn_recorder.record_gsyn:
extra_mallocs += 1
if self._v_recorder.record_v:
extra_mallocs += 1
if self._spike_recorder.record:
extra_mallocs += 1
return (
2 + self._synapse_manager.get_number_of_mallocs_used_by_dsg() +
extra_mallocs)
def _get_number_of_mallocs_from_basic_model(self):
# one for system, one for neuron params
return 2
def _reserve_memory_regions(
self, spec, vertex_slice, spike_history_region_sz,
v_history_region_sz, gsyn_history_region_sz):
spec.comment("\nReserving memory space for data regions:\n\n")
# Reserve memory:
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.SYSTEM.value,
size=((constants.DATA_SPECABLE_BASIC_SETUP_INFO_N_WORDS * 4) +
self.get_recording_data_size(3)), label='System')
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value,
size=self._get_sdram_usage_for_neuron_params(vertex_slice),
label='NeuronParams')
self.reserve_buffer_regions(
spec,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
[constants.POPULATION_BASED_REGIONS.SPIKE_HISTORY.value,
constants.POPULATION_BASED_REGIONS.POTENTIAL_HISTORY.value,
constants.POPULATION_BASED_REGIONS.GSYN_HISTORY.value],
[spike_history_region_sz, v_history_region_sz,
gsyn_history_region_sz])
def _write_setup_info(
self, spec, spike_history_region_sz, neuron_potential_region_sz,
gsyn_region_sz, ip_tags, buffer_size_before_receive,
time_between_requests):
""" Write information used to control the simulation and gathering of\
results.
"""
# Write this to the system region (to be picked up by the simulation):
self._write_basic_setup_info(
spec, constants.POPULATION_BASED_REGIONS.SYSTEM.value)
self.write_recording_data(
spec, ip_tags,
[spike_history_region_sz, neuron_potential_region_sz,
gsyn_region_sz], buffer_size_before_receive,
time_between_requests)
def _write_neuron_parameters(
self, spec, key, vertex_slice):
n_atoms = (vertex_slice.hi_atom - vertex_slice.lo_atom) + 1
spec.comment("\nWriting Neuron Parameters for {} Neurons:\n".format(
n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the global parameters
global_params = self._neuron_model.get_global_parameters()
for param in global_params:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the neuron parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._neuron_model.get_neural_parameters())
# Write the input type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._input_type.get_input_type_parameters())
# Write the additional input parameters
if self._additional_input is not None:
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._additional_input.get_parameters())
# Write the threshold type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._threshold_type.get_threshold_parameters())
def _get_recording_and_buffer_sizes(self, buffer_max, space_needed):
if space_needed == 0:
return 0, False
if not self._enable_buffered_recording:
return space_needed, False
if buffer_max < space_needed:
return buffer_max, True
return space_needed, False
# @implements AbstractDataSpecableVertex.generate_data_spec
def generate_data_spec(
self, subvertex, placement, subgraph, graph, routing_info,
hostname, graph_mapper, report_folder, ip_tags,
reverse_ip_tags, write_text_specs, application_run_time_folder):
# Create new DataSpec for this processor:
data_writer, report_writer = self.get_data_spec_file_writers(
placement.x, placement.y, placement.p, hostname, report_folder,
write_text_specs, application_run_time_folder)
spec = DataSpecificationGenerator(data_writer, report_writer)
spec.comment("\n*** Spec for block of {} neurons ***\n".format(
self.model_name))
vertex_slice = graph_mapper.get_subvertex_slice(subvertex)
# Get recording sizes - the order is important here as spikes will
# require less space than voltage and voltage less than gsyn. This
# order ensures that the buffer size before receive is optimum for
# all recording channels
# TODO: Maybe split the buffer size before receive by channel?
spike_history_sz, spike_buffering_needed = \
self._get_recording_and_buffer_sizes(
self._spike_buffer_max_size,
self._spike_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps))
v_history_sz, v_buffering_needed = \
self._get_recording_and_buffer_sizes(
self._v_buffer_max_size,
self._v_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps))
gsyn_history_sz, gsyn_buffering_needed = \
self._get_recording_and_buffer_sizes(
self._gsyn_buffer_max_size,
self._gsyn_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps))
buffer_size_before_receive = self._buffer_size_before_receive
if (not spike_buffering_needed and not v_buffering_needed and
not gsyn_buffering_needed):
buffer_size_before_receive = max((
spike_history_sz, v_history_sz, gsyn_history_sz)) + 256
# Reserve memory regions
self._reserve_memory_regions(
spec, vertex_slice, spike_history_sz, v_history_sz,
gsyn_history_sz)
# Declare random number generators and distributions:
# TODO add random distribution stuff
# self.write_random_distribution_declarations(spec)
# Get the key - use only the first edge
key = None
if len(subgraph.outgoing_subedges_from_subvertex(subvertex)) > 0:
keys_and_masks = routing_info.get_keys_and_masks_from_subedge(
subgraph.outgoing_subedges_from_subvertex(subvertex)[0])
# NOTE: using the first key assigned as the key. Should in future
# get the list of keys and use one per neuron, to allow arbitrary
# key and mask assignments
key = keys_and_masks[0].key
# Write the regions
self._write_setup_info(
spec, spike_history_sz, v_history_sz, gsyn_history_sz, ip_tags,
buffer_size_before_receive, self._time_between_requests)
self._write_neuron_parameters(spec, key, vertex_slice)
# allow the synaptic matrix to write its data specable data
self._synapse_manager.write_data_spec(
spec, self, vertex_slice, subvertex, placement, subgraph, graph,
routing_info, hostname, graph_mapper)
# End the writing of this specification:
spec.end_specification()
data_writer.close()
return [data_writer.filename]
# @implements AbstractDataSpecableVertex.get_binary_file_name
def get_binary_file_name(self):
# Split binary name into title and extension
binary_title, binary_extension = os.path.splitext(self._binary)
# Reunite title and extension and return
return (binary_title + self._synapse_manager.vertex_executable_suffix +
binary_extension)
# @implements AbstractSpikeRecordable.is_recording_spikes
def is_recording_spikes(self):
return self._spike_recorder.record
# @implements AbstractSpikeRecordable.set_recording_spikes
def set_recording_spikes(self):
self._change_requires_mapping = not self._spike_recorder.record
self.set_buffering_output(
self._receive_buffer_host, self._receive_buffer_port)
self._spike_recorder.record = True
# @implements AbstractSpikeRecordable.get_spikes
def get_spikes(self, placements, graph_mapper, buffer_manager):
return self._spike_recorder.get_spikes(
self._label, buffer_manager,
constants.POPULATION_BASED_REGIONS.SPIKE_HISTORY.value,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
placements, graph_mapper, self)
# @implements AbstractVRecordable.is_recording_v
def is_recording_v(self):
return self._v_recorder.record_v
# @implements AbstractVRecordable.set_recording_v
def set_recording_v(self):
self.set_buffering_output(
self._receive_buffer_host, self._receive_buffer_port)
self._change_requires_mapping = not self._v_recorder.record_v
self._v_recorder.record_v = True
# @implements AbstractVRecordable.get_v
def get_v(self, n_machine_time_steps, placements, graph_mapper,
buffer_manager):
return self._v_recorder.get_v(
self._label, buffer_manager,
constants.POPULATION_BASED_REGIONS.POTENTIAL_HISTORY.value,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
placements, graph_mapper, self)
# @implements AbstractGSynRecordable.is_recording_gsyn
def is_recording_gsyn(self):
return self._gsyn_recorder.record_gsyn
# @implements AbstractGSynRecordable.set_recording_gsyn
def set_recording_gsyn(self):
self.set_buffering_output(
self._receive_buffer_host, self._receive_buffer_port)
self._change_requires_mapping = not self._gsyn_recorder.record_gsyn
self._gsyn_recorder.record_gsyn = True
# @implements AbstractGSynRecordable.get_gsyn
def get_gsyn(self, n_machine_time_steps, placements, graph_mapper,
buffer_manager):
return self._gsyn_recorder.get_gsyn(
self._label, buffer_manager,
constants.POPULATION_BASED_REGIONS.GSYN_HISTORY.value,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
placements, graph_mapper, self)
def initialize(self, variable, value):
initialize_attr = getattr(
self._neuron_model, "initialize_%s" % variable, None)
if initialize_attr is None or not callable(initialize_attr):
raise Exception("Vertex does not support initialisation of"
" parameter {}".format(variable))
initialize_attr(value)
self._change_requires_mapping = True
@property
def synapse_type(self):
return self._synapse_manager.synapse_type
@property
def input_type(self):
return self._input_type
def get_value(self, key):
""" Get a property of the overall model
"""
for obj in [self._neuron_model, self._input_type,
self._threshold_type, self._synapse_manager.synapse_type,
self._additional_input]:
if hasattr(obj, key):
return getattr(obj, key)
raise Exception("Population {} does not have parameter {}".format(
self.vertex, key))
def set_value(self, key, value):
""" Set a property of the overall model
"""
for obj in [self._neuron_model, self._input_type,
self._threshold_type, self._synapse_manager.synapse_type,
self._additional_input]:
if hasattr(obj, key):
setattr(obj, key, value)
self._change_requires_mapping = True
return
raise Exception("Type {} does not have parameter {}".format(
self._model_name, key))
@property
def weight_scale(self):
return self._input_type.get_global_weight_scale()
@property
def ring_buffer_sigma(self):
return self._synapse_manager.ring_buffer_sigma
@ring_buffer_sigma.setter
def ring_buffer_sigma(self, ring_buffer_sigma):
self._synapse_manager.ring_buffer_sigma = ring_buffer_sigma
@property
def spikes_per_second(self):
return self._synapse_manager.spikes_per_second
@spikes_per_second.setter
def spikes_per_second(self, spikes_per_second):
self._synapse_manager.spikes_per_second = spikes_per_second
def get_synaptic_list_from_machine(
self, placements, transceiver, pre_subvertex, pre_n_atoms,
post_subvertex, synapse_io, subgraph, routing_infos,
weight_scales):
return self._synapse_manager.get_synaptic_list_from_machine(
placements, transceiver, pre_subvertex, pre_n_atoms,
post_subvertex, synapse_io, subgraph, routing_infos, weight_scales)
def is_data_specable(self):
return True
def get_incoming_edge_constraints(self, partitioned_edge, graph_mapper):
""" Gets the constraints for edges going into this vertex
:param partitioned_edge: partitioned edge that goes into this vertex
:param graph_mapper: the graph mapper object
:return: list of constraints
"""
return self._synapse_manager.get_incoming_edge_constraints()
def get_outgoing_edge_constraints(self, partitioned_edge, graph_mapper):
""" Gets the constraints for edges going out of this vertex
:param partitioned_edge: the partitioned edge that leaves this vertex
:param graph_mapper: the graph mapper object
:return: list of constraints
"""
return [KeyAllocatorContiguousRangeContraint()]
def __str__(self):
return "{} with {} atoms".format(self._label, self.n_atoms)
def __repr__(self):
return self.__str__()
class AbstractPopulationVertex(
AbstractPartitionableVertex, AbstractDataSpecableVertex,
AbstractSpikeRecordable, AbstractVRecordable, AbstractGSynRecordable,
AbstractProvidesOutgoingPartitionConstraints,
AbstractProvidesIncomingPartitionConstraints,
AbstractPopulationInitializable, AbstractPopulationSettable,
AbstractChangableAfterRun):
""" Underlying vertex model for Neural Populations.
"""
def __init__(self,
n_neurons,
binary,
label,
max_atoms_per_core,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
model_name,
neuron_model,
input_type,
synapse_type,
threshold_type,
additional_input=None,
constraints=None):
AbstractPartitionableVertex.__init__(self, n_neurons, label,
max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(self, machine_time_step,
timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
AbstractProvidesIncomingPartitionConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._incoming_spike_buffer_size = incoming_spike_buffer_size
if incoming_spike_buffer_size is None:
self._incoming_spike_buffer_size = config.getint(
"Simulation", "incoming_spike_buffer_size")
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
self._v_buffer_max_size = config.getint("Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint("Buffers",
"gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = config.getint("Buffers",
"receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# Set up synapse handling
self._synapse_manager = SynapticManager(synapse_type,
machine_time_step,
ring_buffer_sigma,
spikes_per_second)
# bool for if state has changed.
self._change_requires_mapping = True
@property
def requires_mapping(self):
return self._change_requires_mapping
def mark_no_changes(self):
self._change_requires_mapping = False
def create_subvertex(self,
vertex_slice,
resources_required,
label=None,
constraints=None):
is_recording = (self._gsyn_recorder.record_gsyn
or self._v_recorder.record_v
or self._spike_recorder.record)
subvertex = PopulationPartitionedVertex(resources_required, label,
is_recording, constraints)
if not self._using_auto_pause_and_resume:
spike_buffer_size = self._spike_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
v_buffer_size = self._v_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
gsyn_buffer_size = self._gsyn_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
spike_buffering_needed = recording_utils.needs_buffering(
self._spike_buffer_max_size, spike_buffer_size,
self._enable_buffered_recording)
v_buffering_needed = recording_utils.needs_buffering(
self._v_buffer_max_size, v_buffer_size,
self._enable_buffered_recording)
gsyn_buffering_needed = recording_utils.needs_buffering(
self._gsyn_buffer_max_size, gsyn_buffer_size,
self._enable_buffered_recording)
if (spike_buffering_needed or v_buffering_needed
or gsyn_buffering_needed):
subvertex.activate_buffering_output(
buffering_ip_address=self._receive_buffer_host,
buffering_port=self._receive_buffer_port)
else:
sdram_per_ts = 0
sdram_per_ts += self._spike_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, 1)
sdram_per_ts += self._v_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, 1)
sdram_per_ts += self._gsyn_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, 1)
subvertex.activate_buffering_output(
minimum_sdram_for_buffering=self._minimum_buffer_sdram,
buffered_sdram_per_timestep=sdram_per_ts)
return subvertex
@property
def maximum_delay_supported_in_ms(self):
return self._synapse_manager.maximum_delay_supported_in_ms
# @implements AbstractPopulationVertex.get_cpu_usage_for_atoms
def get_cpu_usage_for_atoms(self, vertex_slice, graph):
per_neuron_cycles = (
_NEURON_BASE_N_CPU_CYCLES_PER_NEURON +
self._neuron_model.get_n_cpu_cycles_per_neuron() +
self._input_type.get_n_cpu_cycles_per_neuron(
self._synapse_manager.synapse_type.get_n_synapse_types()) +
self._threshold_type.get_n_cpu_cycles_per_neuron())
if self._additional_input is not None:
per_neuron_cycles += \
self._additional_input.get_n_cpu_cycles_per_neuron()
return (_NEURON_BASE_N_CPU_CYCLES + _C_MAIN_BASE_N_CPU_CYCLES +
(per_neuron_cycles * vertex_slice.n_atoms) +
self._spike_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._v_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._gsyn_recorder.get_n_cpu_cycles(vertex_slice.n_atoms) +
self._synapse_manager.get_n_cpu_cycles(vertex_slice, graph))
# @implements AbstractPopulationVertex.get_dtcm_usage_for_atoms
def get_dtcm_usage_for_atoms(self, vertex_slice, graph):
per_neuron_usage = (
self._neuron_model.get_dtcm_usage_per_neuron_in_bytes() +
self._input_type.get_dtcm_usage_per_neuron_in_bytes() +
self._threshold_type.get_dtcm_usage_per_neuron_in_bytes())
if self._additional_input is not None:
per_neuron_usage += \
self._additional_input.get_dtcm_usage_per_neuron_in_bytes()
return (
_NEURON_BASE_DTCM_USAGE_IN_BYTES +
(per_neuron_usage * vertex_slice.n_atoms) +
self._spike_recorder.get_dtcm_usage_in_bytes() +
self._v_recorder.get_dtcm_usage_in_bytes() +
self._gsyn_recorder.get_dtcm_usage_in_bytes() +
self._synapse_manager.get_dtcm_usage_in_bytes(vertex_slice, graph))
def _get_sdram_usage_for_neuron_params(self, vertex_slice):
per_neuron_usage = (
self._input_type.get_sdram_usage_per_neuron_in_bytes() +
self._threshold_type.get_sdram_usage_per_neuron_in_bytes())
if self._additional_input is not None:
per_neuron_usage += \
self._additional_input.get_sdram_usage_per_neuron_in_bytes()
return (
(common_constants.DATA_SPECABLE_BASIC_SETUP_INFO_N_WORDS * 4) +
ReceiveBuffersToHostBasicImpl.get_recording_data_size(3) +
(per_neuron_usage * vertex_slice.n_atoms) +
self._neuron_model.get_sdram_usage_in_bytes(vertex_slice.n_atoms))
# @implements AbstractPartitionableVertex.get_sdram_usage_for_atoms
def get_sdram_usage_for_atoms(self, vertex_slice, graph):
sdram_requirement = (
self._get_sdram_usage_for_neuron_params(vertex_slice) +
ReceiveBuffersToHostBasicImpl.get_buffer_state_region_size(3) +
PopulationPartitionedVertex.get_provenance_data_size(
PopulationPartitionedVertex.N_ADDITIONAL_PROVENANCE_DATA_ITEMS)
+ self._synapse_manager.get_sdram_usage_in_bytes(
vertex_slice, graph.incoming_edges_to_vertex(self)) +
(self._get_number_of_mallocs_used_by_dsg(
vertex_slice, graph.incoming_edges_to_vertex(self)) *
common_constants.SARK_PER_MALLOC_SDRAM_USAGE))
# add recording SDRAM if not automatically calculated
if not self._using_auto_pause_and_resume:
spike_buffer_size = self._spike_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
v_buffer_size = self._v_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
gsyn_buffer_size = self._gsyn_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
sdram_requirement += recording_utils.get_buffer_sizes(
self._spike_buffer_max_size, spike_buffer_size,
self._enable_buffered_recording)
sdram_requirement += recording_utils.get_buffer_sizes(
self._v_buffer_max_size, v_buffer_size,
self._enable_buffered_recording)
sdram_requirement += recording_utils.get_buffer_sizes(
self._gsyn_buffer_max_size, gsyn_buffer_size,
self._enable_buffered_recording)
else:
sdram_requirement += self._minimum_buffer_sdram
return sdram_requirement
# @implements AbstractPopulationVertex.model_name
def model_name(self):
return self._model_name
def _get_number_of_mallocs_used_by_dsg(self, vertex_slice, in_edges):
extra_mallocs = 0
if self._gsyn_recorder.record_gsyn:
extra_mallocs += 1
if self._v_recorder.record_v:
extra_mallocs += 1
if self._spike_recorder.record:
extra_mallocs += 1
return (2 + self._synapse_manager.get_number_of_mallocs_used_by_dsg() +
extra_mallocs)
def _get_number_of_mallocs_from_basic_model(self):
# one for system, one for neuron params
return 2
def _reserve_memory_regions(self, spec, vertex_slice,
spike_history_region_sz, v_history_region_sz,
gsyn_history_region_sz, subvertex):
spec.comment("\nReserving memory space for data regions:\n\n")
# Reserve memory:
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.SYSTEM.value,
size=(
(common_constants.DATA_SPECABLE_BASIC_SETUP_INFO_N_WORDS * 4) +
subvertex.get_recording_data_size(3)),
label='System')
spec.reserve_memory_region(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value,
size=self._get_sdram_usage_for_neuron_params(vertex_slice),
label='NeuronParams')
subvertex.reserve_buffer_regions(
spec, constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
[
constants.POPULATION_BASED_REGIONS.SPIKE_HISTORY.value,
constants.POPULATION_BASED_REGIONS.POTENTIAL_HISTORY.value,
constants.POPULATION_BASED_REGIONS.GSYN_HISTORY.value
], [
spike_history_region_sz, v_history_region_sz,
gsyn_history_region_sz
])
subvertex.reserve_provenance_data_region(spec)
def _write_setup_info(self, spec, spike_history_region_sz,
neuron_potential_region_sz, gsyn_region_sz, ip_tags,
buffer_size_before_receive, time_between_requests,
subvertex):
""" Write information used to control the simulation and gathering of\
results.
"""
# Write this to the system region (to be picked up by the simulation):
self._write_basic_setup_info(
spec, constants.POPULATION_BASED_REGIONS.SYSTEM.value)
subvertex.write_recording_data(spec, ip_tags, [
spike_history_region_sz, neuron_potential_region_sz, gsyn_region_sz
], buffer_size_before_receive, time_between_requests)
def _write_neuron_parameters(self, spec, key, vertex_slice):
n_atoms = (vertex_slice.hi_atom - vertex_slice.lo_atom) + 1
spec.comment(
"\nWriting Neuron Parameters for {} Neurons:\n".format(n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the size of the incoming spike buffer
spec.write_value(data=self._incoming_spike_buffer_size)
# Write the global parameters
global_params = self._neuron_model.get_global_parameters()
for param in global_params:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the neuron parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._neuron_model.get_neural_parameters())
# Write the input type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._input_type.get_input_type_parameters())
# Write the additional input parameters
if self._additional_input is not None:
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._additional_input.get_parameters())
# Write the threshold type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._threshold_type.get_threshold_parameters())
# @implements AbstractDataSpecableVertex.generate_data_spec
def generate_data_spec(self, subvertex, placement, partitioned_graph,
graph, routing_info, hostname, graph_mapper,
report_folder, ip_tags, reverse_ip_tags,
write_text_specs, application_run_time_folder):
# Create new DataSpec for this processor:
data_writer, report_writer = self.get_data_spec_file_writers(
placement.x, placement.y, placement.p, hostname, report_folder,
write_text_specs, application_run_time_folder)
spec = DataSpecificationGenerator(data_writer, report_writer)
spec.comment("\n*** Spec for block of {} neurons ***\n".format(
self.model_name))
vertex_slice = graph_mapper.get_subvertex_slice(subvertex)
# Get recording sizes - the order is important here as spikes will
# require less space than voltage and voltage less than gsyn. This
# order ensures that the buffer size before receive is optimum for
# all recording channels
# TODO: Maybe split the buffer size before receive by channel?
spike_buffer_size = self._spike_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
v_buffer_size = self._v_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
gsyn_buffer_size = self._gsyn_recorder.get_sdram_usage_in_bytes(
vertex_slice.n_atoms, self._no_machine_time_steps)
spike_history_sz = recording_utils.get_buffer_sizes(
self._spike_buffer_max_size, spike_buffer_size,
self._enable_buffered_recording)
v_history_sz = recording_utils.get_buffer_sizes(
self._v_buffer_max_size, v_buffer_size,
self._enable_buffered_recording)
gsyn_history_sz = recording_utils.get_buffer_sizes(
self._gsyn_buffer_max_size, gsyn_buffer_size,
self._enable_buffered_recording)
spike_buffering_needed = recording_utils.needs_buffering(
self._spike_buffer_max_size, spike_buffer_size,
self._enable_buffered_recording)
v_buffering_needed = recording_utils.needs_buffering(
self._v_buffer_max_size, v_buffer_size,
self._enable_buffered_recording)
gsyn_buffering_needed = recording_utils.needs_buffering(
self._gsyn_buffer_max_size, gsyn_buffer_size,
self._enable_buffered_recording)
buffer_size_before_receive = self._buffer_size_before_receive
if (not spike_buffering_needed and not v_buffering_needed
and not gsyn_buffering_needed):
buffer_size_before_receive = max(
(spike_history_sz, v_history_sz, gsyn_history_sz)) + 256
# Reserve memory regions
self._reserve_memory_regions(spec, vertex_slice, spike_history_sz,
v_history_sz, gsyn_history_sz, subvertex)
# Declare random number generators and distributions:
# TODO add random distribution stuff
# self.write_random_distribution_declarations(spec)
# Get the key - use only the first edge
key = None
for partition in partitioned_graph.\
outgoing_edges_partitions_from_vertex(subvertex).values():
keys_and_masks = \
routing_info.get_keys_and_masks_from_partition(partition)
# NOTE: using the first key assigned as the key. Should in future
# get the list of keys and use one per neuron, to allow arbitrary
# key and mask assignments
key = keys_and_masks[0].key
# Write the regions
self._write_setup_info(spec, spike_history_sz, v_history_sz,
gsyn_history_sz, ip_tags,
buffer_size_before_receive,
self._time_between_requests, subvertex)
self._write_neuron_parameters(spec, key, vertex_slice)
# allow the synaptic matrix to write its data spec-able data
self._synapse_manager.write_data_spec(spec, self, vertex_slice,
subvertex, placement,
partitioned_graph, graph,
routing_info, graph_mapper,
self._input_type)
# End the writing of this specification:
spec.end_specification()
data_writer.close()
return data_writer.filename
# @implements AbstractDataSpecableVertex.get_binary_file_name
def get_binary_file_name(self):
# Split binary name into title and extension
binary_title, binary_extension = os.path.splitext(self._binary)
# Reunite title and extension and return
return (binary_title + self._synapse_manager.vertex_executable_suffix +
binary_extension)
# @implements AbstractSpikeRecordable.is_recording_spikes
def is_recording_spikes(self):
return self._spike_recorder.record
# @implements AbstractSpikeRecordable.set_recording_spikes
def set_recording_spikes(self):
self._change_requires_mapping = not self._spike_recorder.record
self._spike_recorder.record = True
# @implements AbstractSpikeRecordable.get_spikes
def get_spikes(self, placements, graph_mapper, buffer_manager):
return self._spike_recorder.get_spikes(
self._label, buffer_manager,
constants.POPULATION_BASED_REGIONS.SPIKE_HISTORY.value,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
placements, graph_mapper, self)
# @implements AbstractVRecordable.is_recording_v
def is_recording_v(self):
return self._v_recorder.record_v
# @implements AbstractVRecordable.set_recording_v
def set_recording_v(self):
self._change_requires_mapping = not self._v_recorder.record_v
self._v_recorder.record_v = True
# @implements AbstractVRecordable.get_v
def get_v(self, n_machine_time_steps, placements, graph_mapper,
buffer_manager):
return self._v_recorder.get_v(
self._label, buffer_manager,
constants.POPULATION_BASED_REGIONS.POTENTIAL_HISTORY.value,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
placements, graph_mapper, self)
# @implements AbstractGSynRecordable.is_recording_gsyn
def is_recording_gsyn(self):
return self._gsyn_recorder.record_gsyn
# @implements AbstractGSynRecordable.set_recording_gsyn
def set_recording_gsyn(self):
self._change_requires_mapping = not self._gsyn_recorder.record_gsyn
self._gsyn_recorder.record_gsyn = True
# @implements AbstractGSynRecordable.get_gsyn
def get_gsyn(self, n_machine_time_steps, placements, graph_mapper,
buffer_manager):
return self._gsyn_recorder.get_gsyn(
self._label, buffer_manager,
constants.POPULATION_BASED_REGIONS.GSYN_HISTORY.value,
constants.POPULATION_BASED_REGIONS.BUFFERING_OUT_STATE.value,
placements, graph_mapper, self)
def initialize(self, variable, value):
initialize_attr = getattr(self._neuron_model,
"initialize_%s" % variable, None)
if initialize_attr is None or not callable(initialize_attr):
raise Exception("Vertex does not support initialisation of"
" parameter {}".format(variable))
initialize_attr(value)
self._change_requires_mapping = True
@property
def synapse_type(self):
return self._synapse_manager.synapse_type
@property
def input_type(self):
return self._input_type
def get_value(self, key):
""" Get a property of the overall model
"""
for obj in [
self._neuron_model, self._input_type, self._threshold_type,
self._synapse_manager.synapse_type, self._additional_input
]:
if hasattr(obj, key):
return getattr(obj, key)
raise Exception("Population {} does not have parameter {}".format(
self.vertex, key))
def set_value(self, key, value):
""" Set a property of the overall model
"""
for obj in [
self._neuron_model, self._input_type, self._threshold_type,
self._synapse_manager.synapse_type, self._additional_input
]:
if hasattr(obj, key):
setattr(obj, key, value)
self._change_requires_mapping = True
return
raise Exception("Type {} does not have parameter {}".format(
self._model_name, key))
@property
def weight_scale(self):
return self._input_type.get_global_weight_scale()
@property
def ring_buffer_sigma(self):
return self._synapse_manager.ring_buffer_sigma
@ring_buffer_sigma.setter
def ring_buffer_sigma(self, ring_buffer_sigma):
self._synapse_manager.ring_buffer_sigma = ring_buffer_sigma
@property
def spikes_per_second(self):
return self._synapse_manager.spikes_per_second
@spikes_per_second.setter
def spikes_per_second(self, spikes_per_second):
self._synapse_manager.spikes_per_second = spikes_per_second
@property
def synapse_dynamics(self):
return self._synapse_manager.synapse_dynamics
@synapse_dynamics.setter
def synapse_dynamics(self, synapse_dynamics):
self._synapse_manager.synapse_dynamics = synapse_dynamics
def add_pre_run_connection_holder(self, connection_holder, edge,
synapse_info):
self._synapse_manager.add_pre_run_connection_holder(
connection_holder, edge, synapse_info)
def get_connections_from_machine(self, transceiver, placement, subedge,
graph_mapper, routing_infos, synapse_info,
partitioned_graph):
return self._synapse_manager.get_connections_from_machine(
transceiver, placement, subedge, graph_mapper, routing_infos,
synapse_info, partitioned_graph)
def is_data_specable(self):
return True
def get_incoming_partition_constraints(self, partition, graph_mapper):
""" Gets the constraints for partitions going into this vertex
:param partition: partition that goes into this vertex
:param graph_mapper: the graph mapper object
:return: list of constraints
"""
return self._synapse_manager.get_incoming_partition_constraints()
def get_outgoing_partition_constraints(self, partition, graph_mapper):
""" Gets the constraints for partitions going out of this vertex
:param partition: the partition that leaves this vertex
:param graph_mapper: the graph mapper object
:return: list of constraints
"""
return [KeyAllocatorContiguousRangeContraint()]
def __str__(self):
return "{} with {} atoms".format(self._label, self.n_atoms)
def __repr__(self):
return self.__str__()
