def test_retrieve_direct_block(self):
default_config_paths = os.path.join(
os.path.dirname(abstract_spinnaker_common.__file__),
AbstractSpiNNakerCommon.CONFIG_FILE_NAME)
config = conf_loader.load_config(
AbstractSpiNNakerCommon.CONFIG_FILE_NAME, default_config_paths)
key = 0
n_rows = 2
direct_matrix = bytearray(struct.pack("<IIII", 1, 2, 3, 4))
direct_matrix_1_expanded = bytearray(
struct.pack("<IIIIIIII", 0, 1, 0, 1, 0, 1, 0, 2))
direct_matrix_2_expanded = bytearray(
struct.pack("<IIIIIIII", 0, 1, 0, 3, 0, 1, 0, 4))
synaptic_manager = SynapticManager(
synapse_type=None,
ring_buffer_sigma=5.0,
spikes_per_second=100.0,
config=config,
population_table_type=MockMasterPopulationTable(
{key: [(1, 0, True), (1, n_rows * 4, True)]}),
synapse_io=MockSynapseIO())
transceiver = MockTransceiverRawData(direct_matrix)
placement = Placement(None, 0, 0, 1)
data_1, row_len_1 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver,
placement=placement,
master_pop_table_address=0,
indirect_synapses_address=0,
direct_synapses_address=0,
key=key,
n_rows=n_rows,
index=0,
using_extra_monitor_cores=False)
data_2, row_len_2 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver,
placement=placement,
master_pop_table_address=0,
indirect_synapses_address=0,
direct_synapses_address=0,
key=key,
n_rows=n_rows,
index=1,
using_extra_monitor_cores=False)
# Row lengths should be 1
assert row_len_1 == 1
assert row_len_2 == 1
# Check the data retrieved
assert data_1 == direct_matrix_1_expanded
assert data_2 == direct_matrix_2_expanded
def test_retrieve_synaptic_block(self):
default_config_paths = os.path.join(
os.path.dirname(abstract_spinnaker_common.__file__),
AbstractSpiNNakerCommon.CONFIG_FILE_NAME)
config = conf_loader.load_config(
AbstractSpiNNakerCommon.CONFIG_FILE_NAME, default_config_paths)
key = 0
synaptic_manager = SynapticManager(
synapse_type=None, ring_buffer_sigma=5.0, spikes_per_second=100.0,
config=config,
population_table_type=MockMasterPopulationTable(
{key: [(1, 0, False)]}),
synapse_io=MockSynapseIO())
transceiver = MockTransceiverRawData(bytearray(16))
placement = Placement(None, 0, 0, 1)
first_block, row_len_1 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver, placement=placement,
master_pop_table_address=0, indirect_synapses_address=0,
direct_synapses_address=0, key=key, n_rows=1, index=0,
using_extra_monitor_cores=False)
same_block, row_len_1_2 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver, placement=placement,
master_pop_table_address=0, indirect_synapses_address=0,
direct_synapses_address=0, key=key, n_rows=1, index=0,
using_extra_monitor_cores=False)
synaptic_manager.clear_connection_cache()
different_block, row_len_2 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver, placement=placement,
master_pop_table_address=0, indirect_synapses_address=0,
direct_synapses_address=0, key=key, n_rows=1, index=0,
using_extra_monitor_cores=False)
# Check that the row lengths are all the same
assert row_len_1 == row_len_1_2
assert row_len_1 == row_len_2
# Check that the block retrieved twice without reset is cached
assert id(first_block) == id(same_block)
# Check that the block after reset is not a copy
assert id(first_block) != id(different_block)
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")
def test_write_synaptic_matrix_and_master_population_table(self):
MockSimulator.setup()
default_config_paths = os.path.join(
os.path.dirname(abstract_spinnaker_common.__file__),
AbstractSpiNNakerCommon.CONFIG_FILE_NAME)
config = conf_loader.load_config(
AbstractSpiNNakerCommon.CONFIG_FILE_NAME, default_config_paths)
config.set("Simulation", "one_to_one_connection_dtcm_max_bytes", 40)
machine_time_step = 1000.0
pre_app_vertex = SimpleApplicationVertex(10)
pre_vertex = SimpleMachineVertex(resources=None)
pre_vertex_slice = Slice(0, 9)
post_app_vertex = SimpleApplicationVertex(10)
post_vertex = SimpleMachineVertex(resources=None)
post_vertex_slice = Slice(0, 9)
post_slice_index = 0
one_to_one_connector_1 = OneToOneConnector(None)
one_to_one_connector_1.set_projection_information(
pre_app_vertex, post_app_vertex, None, machine_time_step)
one_to_one_connector_1.set_weights_and_delays(1.5, 1.0)
one_to_one_connector_2 = OneToOneConnector(None)
one_to_one_connector_2.set_projection_information(
pre_app_vertex, post_app_vertex, None, machine_time_step)
one_to_one_connector_2.set_weights_and_delays(2.5, 2.0)
all_to_all_connector = AllToAllConnector(None)
all_to_all_connector.set_projection_information(
pre_app_vertex, post_app_vertex, None, machine_time_step)
all_to_all_connector.set_weights_and_delays(4.5, 4.0)
direct_synapse_information_1 = SynapseInformation(
one_to_one_connector_1, SynapseDynamicsStatic(), 0)
direct_synapse_information_2 = SynapseInformation(
one_to_one_connector_2, SynapseDynamicsStatic(), 1)
all_to_all_synapse_information = SynapseInformation(
all_to_all_connector, SynapseDynamicsStatic(), 0)
app_edge = ProjectionApplicationEdge(
pre_app_vertex, post_app_vertex, direct_synapse_information_1)
app_edge.add_synapse_information(direct_synapse_information_2)
app_edge.add_synapse_information(all_to_all_synapse_information)
machine_edge = ProjectionMachineEdge(
app_edge.synapse_information, pre_vertex, post_vertex)
partition_name = "TestPartition"
graph = MachineGraph("Test")
graph.add_vertex(pre_vertex)
graph.add_vertex(post_vertex)
graph.add_edge(machine_edge, partition_name)
graph_mapper = GraphMapper()
graph_mapper.add_vertex_mapping(
pre_vertex, pre_vertex_slice, pre_app_vertex)
graph_mapper.add_vertex_mapping(
post_vertex, post_vertex_slice, post_app_vertex)
graph_mapper.add_edge_mapping(machine_edge, app_edge)
weight_scales = [4096.0, 4096.0]
key = 0
routing_info = RoutingInfo()
routing_info.add_partition_info(PartitionRoutingInfo(
[BaseKeyAndMask(key, 0xFFFFFFF0)],
graph.get_outgoing_edge_partition_starting_at_vertex(
pre_vertex, partition_name)))
temp_spec = tempfile.mktemp()
spec_writer = FileDataWriter(temp_spec)
spec = DataSpecificationGenerator(spec_writer, None)
master_pop_sz = 1000
master_pop_region = 0
all_syn_block_sz = 2000
synapse_region = 1
spec.reserve_memory_region(master_pop_region, master_pop_sz)
spec.reserve_memory_region(synapse_region, all_syn_block_sz)
synapse_type = MockSynapseType()
synaptic_manager = SynapticManager(
synapse_type=synapse_type, ring_buffer_sigma=5.0,
spikes_per_second=100.0, config=config)
synaptic_manager._write_synaptic_matrix_and_master_population_table(
spec, [post_vertex_slice], post_slice_index, post_vertex,
post_vertex_slice, all_syn_block_sz, weight_scales,
master_pop_region, synapse_region, routing_info, graph_mapper,
graph, machine_time_step)
spec.end_specification()
spec_writer.close()
spec_reader = FileDataReader(temp_spec)
executor = DataSpecificationExecutor(
spec_reader, master_pop_sz + all_syn_block_sz)
executor.execute()
master_pop_table = executor.get_region(0)
synaptic_matrix = executor.get_region(1)
all_data = bytearray()
all_data.extend(master_pop_table.region_data[
:master_pop_table.max_write_pointer])
all_data.extend(synaptic_matrix.region_data[
:synaptic_matrix.max_write_pointer])
master_pop_table_address = 0
synaptic_matrix_address = master_pop_table.max_write_pointer
direct_synapses_address = struct.unpack_from(
"<I", synaptic_matrix.region_data)[0]
direct_synapses_address += synaptic_matrix_address + 8
indirect_synapses_address = synaptic_matrix_address + 4
placement = Placement(None, 0, 0, 1)
transceiver = MockTransceiverRawData(all_data)
# Get the master population table details
items = synaptic_manager._poptable_type\
.extract_synaptic_matrix_data_location(
key, master_pop_table_address, transceiver,
placement.x, placement.y)
# The first entry should be direct, but the rest should be indirect;
# the second is potentially direct, but has been restricted by the
# restriction on the size of the direct matrix
assert len(items) == 3
# TODO: This has been changed because direct matrices are disabled!
assert not items[0][2]
assert not items[1][2]
assert not items[2][2]
data_1, row_len_1 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver, placement=placement,
master_pop_table_address=master_pop_table_address,
indirect_synapses_address=indirect_synapses_address,
direct_synapses_address=direct_synapses_address, key=key,
n_rows=pre_vertex_slice.n_atoms, index=0,
using_extra_monitor_cores=False)
connections_1 = synaptic_manager._synapse_io.read_synapses(
direct_synapse_information_1, pre_vertex_slice, post_vertex_slice,
row_len_1, 0, 2, weight_scales, data_1, None,
app_edge.n_delay_stages, machine_time_step)
# The first matrix is a 1-1 matrix, so row length is 1
assert row_len_1 == 1
# Check that all the connections have the right weight and delay
assert len(connections_1) == post_vertex_slice.n_atoms
assert all([conn["weight"] == 1.5 for conn in connections_1])
assert all([conn["delay"] == 1.0 for conn in connections_1])
data_2, row_len_2 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver, placement=placement,
master_pop_table_address=master_pop_table_address,
indirect_synapses_address=indirect_synapses_address,
direct_synapses_address=direct_synapses_address, key=key,
n_rows=pre_vertex_slice.n_atoms, index=1,
using_extra_monitor_cores=False)
connections_2 = synaptic_manager._synapse_io.read_synapses(
direct_synapse_information_2, pre_vertex_slice, post_vertex_slice,
row_len_2, 0, 2, weight_scales, data_2, None,
app_edge.n_delay_stages, machine_time_step)
# The second matrix is a 1-1 matrix, so row length is 1
assert row_len_2 == 1
# Check that all the connections have the right weight and delay
assert len(connections_2) == post_vertex_slice.n_atoms
assert all([conn["weight"] == 2.5 for conn in connections_2])
assert all([conn["delay"] == 2.0 for conn in connections_2])
data_3, row_len_3 = synaptic_manager._retrieve_synaptic_block(
transceiver=transceiver, placement=placement,
master_pop_table_address=master_pop_table_address,
indirect_synapses_address=indirect_synapses_address,
direct_synapses_address=direct_synapses_address, key=key,
n_rows=pre_vertex_slice.n_atoms, index=2,
using_extra_monitor_cores=False)
connections_3 = synaptic_manager._synapse_io.read_synapses(
all_to_all_synapse_information, pre_vertex_slice,
post_vertex_slice, row_len_3, 0, 2, weight_scales, data_3, None,
app_edge.n_delay_stages, machine_time_step)
# The third matrix is an all-to-all matrix, so length is n_atoms
assert row_len_3 == post_vertex_slice.n_atoms
# Check that all the connections have the right weight and delay
assert len(connections_3) == \
post_vertex_slice.n_atoms * pre_vertex_slice.n_atoms
assert all([conn["weight"] == 4.5 for conn in connections_3])
assert all([conn["delay"] == 4.0 for conn in connections_3])
def __init__(self, n_neurons, constraints, label, model, profile,
time_scale_factor):
# Superclasses
ApplicationVertex.__init__(self, label, constraints)
AbstractAcceptsIncomingSynapses.__init__(self)
SimplePopulationSettable.__init__(self)
HandOverToVertex.__init__(self)
AbstractChangableAfterRun.__init__(self)
AbstractSpikeRecordable.__init__(self)
AbstractNeuronRecordable.__init__(self)
AbstractProvidesNKeysForPartition.__init__(self)
self._model = model
self._profile = profile
self._remapping_required = True
self._synapse_dynamics = None
self._n_fibres_per_ihc = None
self._n_group_tree_rows = None
self._ihcan_vertices = list()
self._drnl_vertices = list()
self._final_agg_vertices = list()
self.__synapse_manager = SynapticManager(
self.N_SYNAPSE_TYPES, None, None,
globals_variables.get_simulator().config)
# calculate n fibres per ihcan core
sample_time = time_scale_factor / self._model.fs
# how many channels
self._n_channels = int(self.get_out_going_size() /
self._model.n_fibres_per_ihc)
# process pole freqs
self._pole_freqs = self._process_pole_freqs()
# how many fibres / atoms ran on each ihcan core
self._n_fibres_per_ihcan_core = self.fibres_per_ihcan_core(
sample_time, self._model.n_fibres_per_ihc)
# process all the other internal numbers
atoms_per_row = self.process_internal_numbers()
# read in param file if needed
self._process_param_file(atoms_per_row)
# recording stuff
self._drnl_neuron_recorder = NeuronRecorder(
DRNLMachineVertex.RECORDABLES,
DRNLMachineVertex.get_matrix_scalar_data_types(),
DRNLMachineVertex.get_matrix_output_data_types(), self._n_dnrls)
self._ihcan_neuron_recorder = NeuronRecorder(
IHCANMachineVertex.RECORDABLES,
IHCANMachineVertex.get_matrix_scalar_data_types(),
IHCANMachineVertex.get_matrix_output_data_types(),
self._n_dnrls * self._n_fibres_per_ihc)
# bool for if state has changed.
self._change_requires_mapping = True
self._change_requires_neuron_parameters_reload = False
self._change_requires_data_generation = False
self._has_reset_last = True
# safety check
if self._n_atoms != n_neurons:
raise ConfigurationException(
self.N_NEURON_ERROR.format(n_neurons, self._n_atoms))
# safety stuff
if (self._model.fs / time_scale_factor >
self.MAX_TIME_SCALE_FACTOR_RATIO):
raise Exception(self.FREQUENCY_ERROR)
# write timer period
self._timer_period = (MICRO_TO_SECOND_CONVERSION *
(self._model.seq_size / self._model.fs))
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")
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