def test_new_graph(self):
"""
tests that after building a machine graph, all partitined vertices
and partitioned edges are in existence
"""
vertices = list()
edges = list()
for i in range(10):
vertices.append(
SimpleMachineVertex(ResourceContainer(), "V{}".format(i)))
with self.assertRaises(NotImplementedError):
vertices[1].add_constraint(SameAtomsAsVertexConstraint(
vertices[4]))
vertices[4].add_constraint(SameAtomsAsVertexConstraint(
vertices[1]))
for i in range(5):
edges.append(MachineEdge(vertices[0], vertices[(i + 1)]))
for i in range(5, 10):
edges.append(MachineEdge(vertices[5], vertices[(i + 1) % 10]))
graph = MachineGraph("foo")
graph.add_vertices(vertices)
graph.add_outgoing_edge_partition(
MulticastEdgePartition(identifier="bar", pre_vertex=vertices[0]))
graph.add_outgoing_edge_partition(
MulticastEdgePartition(identifier="bar", pre_vertex=vertices[5]))
graph.add_edges(edges, "bar")
self.graph_there_and_back(graph)
def test_same_atoms_as_vertex_constraint(self):
with self.assertRaises(NotImplementedError):
v1 = SimpleMachineVertex(None, "v1")
v2 = SimpleMachineVertex(None, "v2")
c1 = SameAtomsAsVertexConstraint(v1)
self.assertEqual(c1.vertex, v1)
self.assertEqual(c1, SameAtomsAsVertexConstraint(v1))
self.assertEqual(str(c1), 'SameAtomsAsVertexConstraint(vertex=v1)')
c2 = SameAtomsAsVertexConstraint(v2)
self.assertNotEqual(c1, c2)
self.assertNotEqual(c1, "1.2.3.4")
d = {}
d[c1] = 1
d[c2] = 2
self.assertEqual(len(d), 2)
def __init__(self,
n_neurons,
delay_per_stage,
source_vertex,
machine_time_step,
timescale_factor,
constraints=None,
label="DelayExtension"):
"""
:param n_neurons: the number of neurons
:param delay_per_stage: the delay per stage
:param source_vertex: where messages are coming from
:param machine_time_step: how long is the machine time step
:param timescale_factor: what slowdown factor has been applied
:param constraints: the vertex constraints
:param label: the vertex label
"""
# pylint: disable=too-many-arguments
super(DelayExtensionVertex, self).__init__(label, constraints, 256)
self._source_vertex = source_vertex
self._n_delay_stages = 0
self._delay_per_stage = delay_per_stage
self._delay_generator_data = defaultdict(list)
# atom store
self._n_atoms = n_neurons
# Dictionary of vertex_slice -> delay block for data specification
self._delay_blocks = dict()
self.add_constraint(SameAtomsAsVertexConstraint(source_vertex))
def constraint_from_json(json_dict, graph=None):
if json_dict["class"] == "BoardConstraint":
return BoardConstraint(json_dict["board_address"])
if json_dict["class"] == "ChipAndCoreConstraint":
if "p" in json_dict:
p = json_dict["p"]
else:
p = None
return ChipAndCoreConstraint(json_dict["x"], json_dict["y"], p)
if json_dict["class"] == "ContiguousKeyRangeContraint":
return ContiguousKeyRangeContraint()
if json_dict["class"] == "FixedKeyAndMaskConstraint":
if "key_list_function" in json_dict:
raise NotImplementedError("key_list_function {}".format(
json_dict["key_list_function"]))
return FixedKeyAndMaskConstraint(
key_masks_from_json(json_dict["keys_and_masks"]))
if json_dict["class"] == "FixedMaskConstraint":
return FixedMaskConstraint(json_dict["mask"])
if json_dict["class"] == "FixedVertexAtomsConstraint":
return FixedVertexAtomsConstraint(json_dict["size"])
if json_dict["class"] == "MaxVertexAtomsConstraint":
return MaxVertexAtomsConstraint(json_dict["size"])
if json_dict["class"] == "RadialPlacementFromChipConstraint":
return RadialPlacementFromChipConstraint(json_dict["x"],
json_dict["y"])
if json_dict["class"] == "SameChipAsConstraint":
return SameChipAsConstraint(vertex_lookup(json_dict["vertex"], graph))
if json_dict["class"] == "SameAtomsAsVertexConstraint":
return SameAtomsAsVertexConstraint(
vertex_lookup(json_dict["vertex"], graph))
raise NotImplementedError("constraint {}".format(json_dict["class"]))
def _add_delay_extension(self, pre_synaptic_population,
post_synaptic_population,
max_delay_for_projection, max_delay_per_neuron,
machine_time_step, timescale_factor):
""" Instantiate delay extension component
"""
# pylint: disable=too-many-arguments
# Create a delay extension vertex to do the extra delays
delay_vertex = pre_synaptic_population._internal_delay_vertex
pre_vertex = pre_synaptic_population._get_vertex
if delay_vertex is None:
delay_name = "{}_delayed".format(pre_vertex.label)
delay_vertex = DelayExtensionVertex(pre_vertex.n_atoms,
max_delay_per_neuron,
pre_vertex,
machine_time_step,
timescale_factor,
label=delay_name)
pre_synaptic_population._internal_delay_vertex = delay_vertex
pre_vertex.add_constraint(
SameAtomsAsVertexConstraint(delay_vertex))
self._spinnaker_control.add_application_vertex(delay_vertex)
# Add the edge
delay_afferent_edge = DelayAfferentApplicationEdge(
pre_vertex,
delay_vertex,
label="{}_to_DelayExtension".format(pre_vertex.label))
self._spinnaker_control.add_application_edge(
delay_afferent_edge, constants.SPIKE_PARTITION_ID)
# Ensure that the delay extension knows how many states it will
# support
n_stages = int(
math.ceil(
float(max_delay_for_projection - max_delay_per_neuron) /
float(max_delay_per_neuron)))
if n_stages > delay_vertex.n_delay_stages:
delay_vertex.n_delay_stages = n_stages
# Create the delay edge if there isn't one already
post_vertex = post_synaptic_population._get_vertex
delay_edge = self._find_existing_edge(delay_vertex, post_vertex)
if delay_edge is None:
delay_edge = DelayedApplicationEdge(
delay_vertex,
post_vertex,
self._synapse_information,
label="{}_delayed_to_{}".format(pre_vertex.label,
post_vertex.label))
self._spinnaker_control.add_application_edge(
delay_edge, constants.SPIKE_PARTITION_ID)
else:
delay_edge.add_synapse_information(self._synapse_information)
return delay_edge
def test_operation_with_same_size_as_vertex_constraint_chain(self):
""" Test that a chain of same size constraints works even when the\
order of vertices is not correct for the chain
"""
with self.assertRaises(NotImplementedError):
graph = ApplicationGraph("Test")
vertex_1 = SimpleTestVertex(10, "Vertex_1", 5)
vertex_1.splitter_object = SplitterSliceLegacy()
vertex_2 = SimpleTestVertex(10, "Vertex_2", 4)
vertex_3 = SimpleTestVertex(10, "Vertex_3", 2)
vertex_3.add_constraint(SameAtomsAsVertexConstraint(vertex_2))
vertex_2.add_constraint(SameAtomsAsVertexConstraint(vertex_1))
vertex_2.splitter_object = SplitterSliceLegacy()
vertex_3.splitter_object = SplitterSliceLegacy()
graph.add_vertices([vertex_1, vertex_2, vertex_3])
machine = virtual_machine(width=2, height=2)
splitter_partitioner(graph, machine, plan_n_time_steps=None)
subvertices_1 = list(vertex_1.machine_vertices)
subvertices_2 = list(vertex_2.machine_vertices)
subvertices_3 = list(vertex_3.machine_vertices)
self.assertEqual(len(subvertices_1), len(subvertices_2))
self.assertEqual(len(subvertices_2), len(subvertices_3))
def test_operation_with_same_size_as_vertex_constraint_exception(self):
"""
test that a partition same as constraint with different size atoms
causes errors
"""
with self.assertRaises(NotImplementedError):
constrained_vertex = SimpleTestVertex(100, "Constrained")
constrained_vertex.add_constraint(
SameAtomsAsVertexConstraint(self.vert2))
constrained_vertex.splitter_object = SplitterSliceLegacy()
self.graph.add_vertex(constrained_vertex)
self.assertRaises(PacmanPartitionException, splitter_partitioner,
self.graph, self.machine, 1000,
PreAllocatedResourceContainer())
def test_operation_with_same_size_as_vertex_constraint(self):
"""
test that the partition and place partitioner can handle same size as
constraints on a vertex that is split into one core
"""
with self.assertRaises(NotImplementedError):
constrained_vertex = SimpleTestVertex(5, "Constrained")
constrained_vertex.add_constraint(
SameAtomsAsVertexConstraint(self.vert2))
constrained_vertex.splitter_object = SplitterSliceLegacy()
self.graph.add_vertex(constrained_vertex)
graph, _ = splitter_partitioner(
self.graph,
self.machine,
plan_n_time_steps=100,
pre_allocated_resources=PreAllocatedResourceContainer())
self.assertEqual(len(list(graph.vertices)), 4)
def test_operation_with_same_size_as_vertex_constraint_large_vertices(
self):
"""
test that the partition and place partitioner can handle same size as
constraints on a vertex which has to be split over many cores
"""
with self.assertRaises(NotImplementedError):
constrained_vertex = SimpleTestVertex(300, "Constrained")
new_large_vertex = SimpleTestVertex(300, "Non constrained")
constrained_vertex.add_constraint(
SameAtomsAsVertexConstraint(new_large_vertex))
new_large_vertex.splitter_object = SplitterSliceLegacy()
constrained_vertex.splitter_object = SplitterSliceLegacy()
self.graph.add_vertices([new_large_vertex, constrained_vertex])
graph, _ = splitter_partitioner(
self.graph,
self.machine,
plan_n_time_steps=100,
pre_allocated_resources=PreAllocatedResourceContainer())
self.assertEqual(len(list(graph.vertices)), 7)
def __init__(self, n_neurons, delay_per_stage, source_vertex,
machine_time_step, timescale_factor, constraints=None,
label="DelayExtension"):
"""Creates a new DelayExtension Object.
"""
# pylint: disable=too-many-arguments
super(DelayExtensionVertex, self).__init__(label, constraints, 256)
self._source_vertex = source_vertex
self._n_delay_stages = 0
self._delay_per_stage = delay_per_stage
# atom store
self._n_atoms = n_neurons
# Dictionary of vertex_slice -> delay block for data specification
self._delay_blocks = dict()
self.add_constraint(
SameAtomsAsVertexConstraint(source_vertex))
def __init__(self,
n_neurons,
delay_per_stage,
source_vertex,
machine_time_step,
time_scale_factor,
constraints=None,
label="DelayExtension"):
"""
:param int n_neurons: the number of neurons
:param int delay_per_stage: the delay per stage
:param ~pacman.model.graphs.application.ApplicationVertex \
source_vertex:
where messages are coming from
:param int machine_time_step: how long is the machine time step
:param int time_scale_factor: what slowdown factor has been applied
:param iterable(~pacman.model.constraints.AbstractConstraint) \
constraints:
the vertex constraints
:param str label: the vertex label
"""
# pylint: disable=too-many-arguments
super(DelayExtensionVertex, self).__init__(label, constraints, 256)
self.__source_vertex = source_vertex
self.__n_delay_stages = 0
self.__delay_per_stage = delay_per_stage
self.__delay_generator_data = defaultdict(list)
self.__machine_time_step = machine_time_step
self.__time_scale_factor = time_scale_factor
self.__n_subvertices = 0
self.__n_data_specs = 0
# atom store
self.__n_atoms = n_neurons
# Dictionary of vertex_slice -> delay block for data specification
self.__delay_blocks = dict()
self.add_constraint(SameAtomsAsVertexConstraint(source_vertex))
def test_operation_same_size_as_vertex_constraint_different_order(self):
"""
test that the partition and place partitioner can handle same size as
constraints on a vertex which has to be split over many cores where
the order of the vertices being added is different.
"""
with self.assertRaises(NotImplementedError):
self.setup()
constrained_vertex = SimpleTestVertex(300, "Constrained")
new_large_vertex = SimpleTestVertex(300, "Non constrained")
constrained_vertex.add_constraint(
SameAtomsAsVertexConstraint(new_large_vertex))
constrained_vertex.splitter_object = SplitterSliceLegacy()
new_large_vertex.splitter_object = SplitterSliceLegacy()
self.graph.add_vertices([constrained_vertex, new_large_vertex])
graph, _ = self.bp(
self.graph,
self.machine,
plan_n_time_steps=100,
pre_allocated_resources=PreAllocatedResourceContainer())
# split in 256 each, so 4 machine vertices
self.assertEqual(len(list(graph.vertices)), 7)
def test_same_atoms_as_vertex_constraint(self):
with self.assertRaises(NotImplementedError):
v1 = SimpleMachineVertex(None, "v1")
c1 = SameAtomsAsVertexConstraint(v1)
self.constraint_there_and_back(c1)
