def run(self, subgraph, graph_mapper):
new_sub_graph = PartitionedGraph(label=subgraph.label)
new_graph_mapper = GraphMapper(graph_mapper.first_graph_label,
subgraph.label)
# create progress bar
progress_bar = ProgressBar(
len(subgraph.subvertices) + len(subgraph.subedges),
"Filtering edges")
# add the subverts directly, as they wont be pruned.
for subvert in subgraph.subvertices:
new_sub_graph.add_subvertex(subvert)
associated_vertex = graph_mapper.get_vertex_from_subvertex(subvert)
vertex_slice = graph_mapper.get_subvertex_slice(subvert)
new_graph_mapper.add_subvertex(
subvertex=subvert, vertex_slice=vertex_slice,
vertex=associated_vertex)
progress_bar.update()
# start checking subedges to decide which ones need pruning....
for subedge in subgraph.subedges:
if not self._is_filterable(subedge, graph_mapper):
logger.debug("this subedge was not pruned {}".format(subedge))
new_sub_graph.add_subedge(subedge)
associated_edge = graph_mapper.\
get_partitionable_edge_from_partitioned_edge(subedge)
new_graph_mapper.add_partitioned_edge(subedge, associated_edge)
else:
logger.debug("this subedge was pruned {}".format(subedge))
progress_bar.update()
progress_bar.end()
# returned the pruned partitioned_graph and graph_mapper
return new_sub_graph, new_graph_mapper
def test_get_vertex_from_subvertex(self):
"""
test that the graph mapper can retribve a vertex froma given subvertex
:return:
"""
subvertices = list()
subvertices.append(PartitionedVertex(None, ""))
subvertices.append(PartitionedVertex(None, ""))
subvert1 = PartitionedVertex(None, "")
subvert2 = PartitionedVertex(None, "")
graph_mapper = GraphMapper()
vert = TestVertex(10, "Some testing vertex")
vertex_slice = Slice(0, 1)
graph_mapper.add_subvertex(subvert1, vertex_slice, vert)
vertex_slice = Slice(2, 3)
graph_mapper.add_subvertex(subvert2, vertex_slice, vert)
self.assertEqual(
vert, graph_mapper.get_vertex_from_subvertex(subvert1))
self.assertEqual(
vert, graph_mapper.get_vertex_from_subvertex(subvert2))
self.assertEqual(
None, graph_mapper.get_vertex_from_subvertex(subvertices[0]))
self.assertEqual(
None, graph_mapper.get_vertex_from_subvertex(subvertices[1]))
def __call__(self, subgraph, graph_mapper):
"""
:param subgraph: the subgraph whose edges are to be filtered
:param graph_mapper: the graph mapper between partitionable and \
partitioned graphs.
:return: a new graph mapper and partitioned graph
"""
new_sub_graph = PartitionedGraph(label=subgraph.label)
new_graph_mapper = GraphMapper(graph_mapper.first_graph_label,
subgraph.label)
# create progress bar
progress_bar = ProgressBar(
len(subgraph.subvertices) + len(subgraph.subedges),
"Filtering edges")
# add the subverts directly, as they wont be pruned.
for subvert in subgraph.subvertices:
new_sub_graph.add_subvertex(subvert)
associated_vertex = graph_mapper.get_vertex_from_subvertex(subvert)
vertex_slice = graph_mapper.get_subvertex_slice(subvert)
new_graph_mapper.add_subvertex(
subvertex=subvert, vertex_slice=vertex_slice,
vertex=associated_vertex)
progress_bar.update()
# start checking subedges to decide which ones need pruning....
for subvert in subgraph.subvertices:
out_going_partitions = \
subgraph.outgoing_edges_partitions_from_vertex(subvert)
for partitioner_identifier in out_going_partitions:
for subedge in \
out_going_partitions[partitioner_identifier].edges:
if not self._is_filterable(subedge, graph_mapper):
logger.debug("this subedge was not pruned {}"
.format(subedge))
new_sub_graph.add_subedge(subedge,
partitioner_identifier)
associated_edge = graph_mapper.\
get_partitionable_edge_from_partitioned_edge(
subedge)
new_graph_mapper.add_partitioned_edge(
subedge, associated_edge)
else:
logger.debug("this subedge was pruned {}"
.format(subedge))
progress_bar.update()
progress_bar.end()
# returned the pruned partitioned_graph and graph_mapper
return {'new_sub_graph': new_sub_graph,
'new_graph_mapper': new_graph_mapper}
def test_many_subvertices(self):
subvertices = list()
for i in range(20 * 17): #51 atoms per each processor on 20 chips
subvertices.append(PartitionedVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"Subvertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = RadialPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
placements = self.bp.place(self.subgraph, self.graph_mapper)
unorderdered_info = list()
for placement in placements.placements:
unorderdered_info.append(
(placement.subvertex.label.split(" ")[0],
"{:<4}".format(placement.subvertex.label.split(" ")[1]),
placement.subvertex.n_atoms, 'x: ',
placement.x, 'y: ', placement.y, 'p: ', placement.p))
from operator import itemgetter
sorted_info = sorted(unorderdered_info, key=itemgetter(4, 6, 8))
from pprint import pprint as pp
pp(sorted_info)
pp("{}".format("=" * 50))
sorted_info = sorted(unorderdered_info, key=lambda x: int(x[1]))
pp(sorted_info)
def test_place_where_subvertices_have_vertices(self):
self.bp = BasicPlacer(self.machine, self.graph)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices, self.vert1)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
def test_place_subvertex_too_big_with_vertex(self):
large_vertex = TestVertex(500, "Large vertex 500")
large_subvertex = large_vertex.create_subvertex(
0, 499, get_resources_used_by_atoms(0, 499, []))#PartitionedVertex(0, 499, "Large subvertex")
self.graph.add_vertex(large_vertex)
self.graph = PartitionableGraph("Graph",[large_vertex])
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices([large_subvertex], large_vertex)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=[large_subvertex])
with self.assertRaises(PacmanPlaceException):
placements = self.bp.place(self.subgraph, self.graph_mapper)
def test_get_subvertices_from_vertex(self):
"""
test getting the subvertex from a graph mappert via the vertex
:return:
"""
subvertices = list()
subvertices.append(PartitionedVertex(None, ""))
subvertices.append(PartitionedVertex(None, ""))
subvert1 = PartitionedVertex(None, "")
subvert2 = PartitionedVertex(None, "")
subedges = list()
subedges.append(MultiCastPartitionedEdge(subvertices[0],
subvertices[1]))
subedges.append(MultiCastPartitionedEdge(subvertices[1],
subvertices[1]))
graph_mapper = GraphMapper()
vert = TestVertex(4, "Some testing vertex")
vertex_slice = Slice(0, 1)
graph_mapper.add_subvertex(subvert1, vertex_slice, vert)
vertex_slice = Slice(2, 3)
graph_mapper.add_subvertex(subvert2, vertex_slice, vert)
returned_subverts = graph_mapper.get_subvertices_from_vertex(vert)
self.assertIn(subvert1, returned_subverts)
self.assertIn(subvert2, returned_subverts)
for sub in subvertices:
self.assertNotIn(sub, returned_subverts)
def test_get_subedges_from_edge(self):
"""
test getting the subedges from a graph mapper from a edge
:return:
"""
subvertices = list()
subedges = list()
subvertices.append(PartitionedVertex(None, ""))
subvertices.append(PartitionedVertex(None, ""))
subedges.append(MultiCastPartitionedEdge(subvertices[0],
subvertices[1]))
subedges.append(MultiCastPartitionedEdge(subvertices[1],
subvertices[1]))
sube = MultiCastPartitionedEdge(subvertices[1], subvertices[0])
subedges.append(sube)
graph = GraphMapper()
edge = TestPartitionableEdge(TestVertex(10, "pre"),
TestVertex(5, "post"))
graph.add_partitioned_edge(sube, edge)
graph.add_partitioned_edge(subedges[0], edge)
subedges_from_edge = \
graph.get_partitioned_edges_from_partitionable_edge(edge)
self.assertIn(sube, subedges_from_edge)
self.assertIn(subedges[0], subedges_from_edge)
self.assertNotIn(subedges[1], subedges_from_edge)
def test_fill_machine(self):
subvertices = list()
for i in range(99 * 17): #50 atoms per each processor on 20 chips
subvertices.append(PartitionedTestVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"PartitionedVertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
placements = self.bp.place(self.subgraph, self.graph_mapper)
def test_too_many_subvertices(self):
subvertices = list()
for i in range(100 * 17): #50 atoms per each processor on 20 chips
subvertices.append(PartitionedVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"Subvertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = RadialPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
with self.assertRaises(PacmanPlaceException):
placements = self.bp.place(self.subgraph, self.graph_mapper)
def test_many_subvertices(self):
subvertices = list()
for i in range(20 * 17): #50 atoms per each processor on 20 chips
subvertices.append(PartitionedTestVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"PartitionedVertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
def __call__(self, subgraph, graph_mapper):
"""
:param subgraph: the subgraph whose edges are to be filtered
:param graph_mapper: the graph mapper between partitionable and \
partitioned graphs.
:return: a new graph mapper and partitioned graph
"""
new_sub_graph = PartitionedGraph(label=subgraph.label)
new_graph_mapper = GraphMapper(graph_mapper.first_graph_label,
subgraph.label)
# create progress bar
progress_bar = ProgressBar(
len(subgraph.subvertices) + len(subgraph.subedges),
"Filtering edges")
# add the subverts directly, as they wont be pruned.
for subvert in subgraph.subvertices:
new_sub_graph.add_subvertex(subvert)
associated_vertex = graph_mapper.get_vertex_from_subvertex(subvert)
vertex_slice = graph_mapper.get_subvertex_slice(subvert)
new_graph_mapper.add_subvertex(subvertex=subvert,
vertex_slice=vertex_slice,
vertex=associated_vertex)
progress_bar.update()
# start checking subedges to decide which ones need pruning....
for subvert in subgraph.subvertices:
out_going_partitions = \
subgraph.outgoing_edges_partitions_from_vertex(subvert)
for partitioner_identifier in out_going_partitions:
for subedge in \
out_going_partitions[partitioner_identifier].edges:
if not self._is_filterable(subedge, graph_mapper):
logger.debug(
"this subedge was not pruned {}".format(subedge))
new_sub_graph.add_subedge(subedge,
partitioner_identifier)
associated_edge = graph_mapper.\
get_partitionable_edge_from_partitioned_edge(
subedge)
new_graph_mapper.add_partitioned_edge(
subedge, associated_edge)
else:
logger.debug(
"this subedge was pruned {}".format(subedge))
progress_bar.update()
progress_bar.end()
# returned the pruned partitioned_graph and graph_mapper
return {
'new_sub_graph': new_sub_graph,
'new_graph_mapper': new_graph_mapper
}
def test_deal_with_constraint_placement_subvertices_have_vertices(self):
self.bp = BasicPlacer(self.machine, self.graph)
self.subvertex1.add_constraint(PlacerChipAndCoreConstraint(1, 5, 2))
self.assertIsInstance(self.subvertex1.constraints[0], PlacerChipAndCoreConstraint)
self.subvertex2.add_constraint(PlacerChipAndCoreConstraint(3, 5, 7))
self.subvertex3.add_constraint(PlacerChipAndCoreConstraint(2, 4, 6))
self.subvertex4.add_constraint(PlacerChipAndCoreConstraint(6, 7, 16))
self.subvertices = list()
self.subvertices.append(self.subvertex1)
self.subvertices.append(self.subvertex2)
self.subvertices.append(self.subvertex3)
self.subvertices.append(self.subvertex4)
self.subedges = list()
self.subgraph = PartitionedGraph("Subgraph", self.subvertices,
self.subedges)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices, self.vert1)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
def test_get_edge_from_subedge(self):
"""
test that tests getting a edge from a graph mapper based off its subedge
:return:
"""
subvertices = list()
subvertices.append(PartitionedVertex(None, ""))
subvertices.append(PartitionedVertex(None, ""))
subedges = list()
subedges.append(MultiCastPartitionedEdge(subvertices[0],
subvertices[1]))
subedges.append(MultiCastPartitionedEdge(subvertices[1],
subvertices[1]))
sube = MultiCastPartitionedEdge(subvertices[1], subvertices[0])
subedges.append(sube)
# Create the graph mapper
graph = GraphMapper()
edge = TestPartitionableEdge(TestVertex(10, "pre"),
TestVertex(5, "post"))
graph.add_partitioned_edge(sube, edge)
graph.add_partitioned_edge(subedges[0], edge)
edge_from_subedge = \
graph.get_partitionable_edge_from_partitioned_edge(sube)
self.assertEqual(edge_from_subedge, edge)
self.assertEqual(
graph.get_partitionable_edge_from_partitioned_edge(subedges[0]),
edge
)
self.assertRaises(
PacmanNotFoundError,
graph.get_partitionable_edge_from_partitioned_edge,
subedges[1]
)
def run(self, subgraph, graph_mapper):
new_sub_graph = PartitionedGraph(label=subgraph.label)
new_graph_mapper = GraphMapper(graph_mapper.first_graph_label,
subgraph.label)
# create progress bar
progress_bar = \
ProgressBar(len(subgraph.subvertices) + len(subgraph.subedges),
"on checking which subedges are filterable given "
"heuristics")
# add the subverts directly, as they wont be pruned.
for subvert in subgraph.subvertices:
new_sub_graph.add_subvertex(subvert)
associated_vertex = graph_mapper.get_vertex_from_subvertex(subvert)
vertex_slice = graph_mapper.get_subvertex_slice(subvert)
new_graph_mapper.add_subvertex(subvertex=subvert,
vertex_slice=vertex_slice,
vertex=associated_vertex)
progress_bar.update()
# start checking subedges to decide which ones need pruning....
for subedge in subgraph.subedges:
if not self._is_filterable(subedge, graph_mapper):
logger.debug("this subedge was not pruned {}".format(subedge))
new_sub_graph.add_subedge(subedge)
associated_edge = graph_mapper.\
get_partitionable_edge_from_partitioned_edge(subedge)
new_graph_mapper.add_partitioned_edge(subedge, associated_edge)
else:
logger.debug("this subedge was pruned {}".format(subedge))
progress_bar.update()
progress_bar.end()
# returned the pruned partitioned_graph and graph_mapper
return new_sub_graph, new_graph_mapper
def __call__(self, graph, machine):
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=graph.vertices,
supported_constraints=[PartitionerMaximumSizeConstraint],
abstract_constraint_type=AbstractPartitionerConstraint)
# start progress bar
progress_bar = ProgressBar(len(graph.vertices),
"Partitioning graph vertices")
vertices = graph.vertices
subgraph = PartitionedGraph(label="partitioned_graph for partitionable"
"_graph {}".format(graph.label))
graph_to_subgraph_mapper = GraphMapper(graph.label, subgraph.label)
resource_tracker = ResourceTracker(machine)
# Partition one vertex at a time
for vertex in vertices:
# Get the usage of the first atom, then assume that this
# will be the usage of all the atoms
requirements = vertex.get_resources_used_by_atoms(Slice(0, 1),
graph)
# Locate the maximum resources available
max_resources_available = \
resource_tracker.get_maximum_constrained_resources_available(
vertex.constraints)
# Find the ratio of each of the resources - if 0 is required,
# assume the ratio is the max available
atoms_per_sdram = self._get_ratio(
max_resources_available.sdram.get_value(),
requirements.sdram.get_value())
atoms_per_dtcm = self._get_ratio(
max_resources_available.dtcm.get_value(),
requirements.dtcm.get_value())
atoms_per_cpu = self._get_ratio(
max_resources_available.cpu.get_value(),
requirements.cpu.get_value())
max_atom_values = [atoms_per_sdram, atoms_per_dtcm, atoms_per_cpu]
max_atoms_constraints = utility_calls.locate_constraints_of_type(
vertex.constraints, PartitionerMaximumSizeConstraint)
for max_atom_constraint in max_atoms_constraints:
max_atom_values.append(max_atom_constraint.size)
atoms_per_core = min(max_atom_values)
# Partition into subvertices
counted = 0
while counted < vertex.n_atoms:
# Determine subvertex size
remaining = vertex.n_atoms - counted
if remaining > atoms_per_core:
alloc = atoms_per_core
else:
alloc = remaining
# Create and store new subvertex, and increment elements
# counted
if counted < 0 or counted + alloc - 1 < 0:
raise PacmanPartitionException("Not enough resources"
" available to create"
" subvertex")
vertex_slice = Slice(counted, counted + (alloc - 1))
subvertex_usage = vertex.get_resources_used_by_atoms(
vertex_slice, graph)
subvert = vertex.create_subvertex(
vertex_slice, subvertex_usage,
"{}:{}:{}".format(vertex.label, counted,
(counted + (alloc - 1))),
partition_algorithm_utilities.
get_remaining_constraints(vertex))
subgraph.add_subvertex(subvert)
graph_to_subgraph_mapper.add_subvertex(
subvert, vertex_slice, vertex)
counted = counted + alloc
# update allocated resources
resource_tracker.allocate_constrained_resources(
subvertex_usage, vertex.constraints)
# update and end progress bars as needed
progress_bar.update()
progress_bar.end()
partition_algorithm_utilities.generate_sub_edges(
subgraph, graph_to_subgraph_mapper, graph)
return {'Partitioned_graph': subgraph,
'Graph_mapper': graph_to_subgraph_mapper}
class TestBasicPlacer(unittest.TestCase):
"""
test for basic placement algorithum
"""
def setUp(self):
########################################################################
# Setting up vertices, edges and graph #
########################################################################
self.vert1 = TestVertex(100, "New AbstractConstrainedTestVertex 1")
self.vert2 = TestVertex(5, "New AbstractConstrainedTestVertex 2")
self.vert3 = TestVertex(3, "New AbstractConstrainedTestVertex 3")
self.edge1 = MultiCastPartitionableEdge(self.vert1, self.vert2,
"First edge")
self.edge2 = MultiCastPartitionableEdge(self.vert2, self.vert1,
"Second edge")
self.edge3 = MultiCastPartitionableEdge(self.vert1, self.vert3,
"Third edge")
self.verts = [self.vert1, self.vert2, self.vert3]
self.edges = [self.edge1, self.edge2, self.edge3]
self.graph = PartitionableGraph("Graph", self.verts, self.edges)
########################################################################
# Setting up machine #
########################################################################
flops = 1000
(e, ne, n, w, sw, s) = range(6)
processors = list()
for i in range(18):
processors.append(Processor(i, flops))
_sdram = SDRAM(128 * (2**20))
ip = "192.168.240.253"
chips = list()
for x in range(10):
for y in range(10):
links = list()
links.append(Link(x, y, 0, (x + 1) % 10, y, n, n))
links.append(Link(x, y, 1, (x + 1) % 10, (y + 1) % 10, s, s))
links.append(Link(x, y, 2, x, (y + 1) % 10, n, n))
links.append(Link(x, y, 3, (x - 1) % 10, y, s, s))
links.append(Link(x, y, 4, (x - 1) % 10, (y - 1) % 10, n, n))
links.append(Link(x, y, 5, x, (y - 1) % 10, s, s))
r = Router(links, False, 100, 1024)
chips.append(Chip(x, y, processors, r, _sdram, 0, 0, ip))
self.machine = Machine(chips)
########################################################################
# Setting up subgraph and graph_mapper #
########################################################################
self.subvertices = list()
self.subvertex1 = PartitionedVertex(
0, 1, self.vert1.get_resources_used_by_atoms(0, 1, []),
"First subvertex")
self.subvertex2 = PartitionedVertex(
1, 5, get_resources_used_by_atoms(1, 5, []), "Second subvertex")
self.subvertex3 = PartitionedVertex(
5, 10, get_resources_used_by_atoms(5, 10, []), "Third subvertex")
self.subvertex4 = PartitionedVertex(
10, 100, get_resources_used_by_atoms(10, 100, []),
"Fourth subvertex")
self.subvertices.append(self.subvertex1)
self.subvertices.append(self.subvertex2)
self.subvertices.append(self.subvertex3)
self.subvertices.append(self.subvertex4)
self.subedges = list()
self.subgraph = PartitionedGraph("Subgraph", self.subvertices,
self.subedges)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices)
@unittest.skip("demonstrating skipping")
def test_new_basic_placer(self):
self.bp = BasicPlacer(self.machine, self.graph)
self.assertEqual(self.bp._machine, self.machine)
self.assertEqual(self.bp._graph, self.graph)
@unittest.skip("demonstrating skipping")
def test_place_where_subvertices_dont_have_vertex(self):
self.bp = BasicPlacer(self.machine, self.graph)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
@unittest.skip("demonstrating skipping")
def test_place_where_subvertices_have_vertices(self):
self.bp = BasicPlacer(self.machine, self.graph)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices, self.vert1)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
@unittest.skip("demonstrating skipping")
def test_place_subvertex_too_big_with_vertex(self):
large_vertex = TestVertex(500, "Large vertex 500")
large_subvertex = large_vertex.create_subvertex(
0, 499, get_resources_used_by_atoms(0, 499, []))#PartitionedVertex(0, 499, "Large subvertex")
self.graph.add_vertex(large_vertex)
self.graph = PartitionableGraph("Graph",[large_vertex])
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices([large_subvertex], large_vertex)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=[large_subvertex])
with self.assertRaises(PacmanPlaceException):
placements = self.bp.place(self.subgraph, self.graph_mapper)
@unittest.skip("demonstrating skipping")
def test_try_to_place(self):
self.assertEqual(True, False, "Test not implemented yet")
@unittest.skip("demonstrating skipping")
def test_deal_with_constraint_placement_subvertices_dont_have_vertex(self):
self.bp = BasicPlacer(self.machine, self.graph)
self.subvertex1.add_constraint(PlacerChipAndCoreConstraint(8, 3, 2))
self.assertIsInstance(self.subvertex1.constraints[0], PlacerChipAndCoreConstraint)
self.subvertex2.add_constraint(PlacerChipAndCoreConstraint(3, 5, 7))
self.subvertex3.add_constraint(PlacerChipAndCoreConstraint(2, 4, 6))
self.subvertex4.add_constraint(PlacerChipAndCoreConstraint(6, 4, 16))
self.subvertices = list()
self.subvertices.append(self.subvertex1)
self.subvertices.append(self.subvertex2)
self.subvertices.append(self.subvertex3)
self.subvertices.append(self.subvertex4)
self.subedges = list()
self.subgraph = PartitionedGraph("Subgraph", self.subvertices,
self.subedges)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
@unittest.skip("demonstrating skipping")
def test_deal_with_constraint_placement_subvertices_have_vertices(self):
self.bp = BasicPlacer(self.machine, self.graph)
self.subvertex1.add_constraint(PlacerChipAndCoreConstraint(1, 5, 2))
self.assertIsInstance(self.subvertex1.constraints[0], PlacerChipAndCoreConstraint)
self.subvertex2.add_constraint(PlacerChipAndCoreConstraint(3, 5, 7))
self.subvertex3.add_constraint(PlacerChipAndCoreConstraint(2, 4, 6))
self.subvertex4.add_constraint(PlacerChipAndCoreConstraint(6, 7, 16))
self.subvertices = list()
self.subvertices.append(self.subvertex1)
self.subvertices.append(self.subvertex2)
self.subvertices.append(self.subvertex3)
self.subvertices.append(self.subvertex4)
self.subedges = list()
self.subgraph = PartitionedGraph("Subgraph", self.subvertices,
self.subedges)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices, self.vert1)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
@unittest.skip("demonstrating skipping")
def test_unsupported_non_placer_constraint(self):
self.assertEqual(True, False, "Test not implemented yet")
@unittest.skip("demonstrating skipping")
def test_unsupported_placer_constraint(self):
self.assertEqual(True, False, "Test not implemented yet")
@unittest.skip("demonstrating skipping")
def test_unsupported_placer_constraints(self):
self.assertEqual(True, False, "Test not implemented yet")
@unittest.skip("demonstrating skipping")
def test_many_subvertices(self):
subvertices = list()
for i in range(20 * 17): #50 atoms per each processor on 20 chips
subvertices.append(PartitionedTestVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"PartitionedVertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
placements = self.bp.place(self.subgraph, self.graph_mapper)
for placement in placements.placements:
print placement.subvertex.label, placement.subvertex.n_atoms, \
'x:', placement.x, 'y:', placement.y, 'p:', placement.p
@unittest.skip("demonstrating skipping")
def test_too_many_subvertices(self):
subvertices = list()
for i in range(100 * 17): #50 atoms per each processor on 20 chips
subvertices.append(PartitionedTestVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"PartitionedVertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
with self.assertRaises(PacmanPlaceException):
placements = self.bp.place(self.subgraph, self.graph_mapper)
@unittest.skip("demonstrating skipping")
def test_fill_machine(self):
subvertices = list()
for i in range(99 * 17): #50 atoms per each processor on 20 chips
subvertices.append(PartitionedTestVertex(
0, 50, get_resources_used_by_atoms(0, 50, []),
"PartitionedVertex " + str(i)))
self.graph = PartitionableGraph("Graph",subvertices)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(subvertices)
self.bp = BasicPlacer(self.machine, self.graph)
self.subgraph = PartitionedGraph(subvertices=subvertices)
placements = self.bp.place(self.subgraph, self.graph_mapper)
def __call__(self, graph, machine):
""" Partition a partitionable_graph so that each subvertex will fit\
on a processor within the machine
:param graph: The partitionable_graph to partition
:type graph:\
:py:class:`pacman.model.graph.partitionable_graph.PartitionableGraph`
:param machine: The machine with respect to which to partition the\
partitionable_graph
:type machine: :py:class:`spinn_machine.machine.Machine`
:return: A partitioned_graph of partitioned vertices and partitioned\
edges
:rtype:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:raise pacman.exceptions.PacmanPartitionException: If something\
goes wrong with the partitioning
"""
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=graph.vertices,
abstract_constraint_type=AbstractPartitionerConstraint,
supported_constraints=[PartitionerMaximumSizeConstraint,
PartitionerSameSizeAsVertexConstraint])
# Load the vertices and create the subgraph to fill
vertices = graph.vertices
subgraph = PartitionedGraph(
label="partitioned graph for {}".format(graph.label))
graph_mapper = GraphMapper(graph.label, subgraph.label)
# sort out vertex's by constraints
vertices = utility_calls.sort_objects_by_constraint_authority(vertices)
# Set up the progress
n_atoms = 0
for vertex in vertices:
n_atoms += vertex.n_atoms
progress_bar = ProgressBar(n_atoms, "Partitioning graph vertices")
resource_tracker = ResourceTracker(machine)
# Partition one vertex at a time
for vertex in vertices:
# check that the vertex hasn't already been partitioned
subverts_from_vertex = \
graph_mapper.get_subvertices_from_vertex(vertex)
# if not, partition
if subverts_from_vertex is None:
self._partition_vertex(vertex, subgraph, graph_mapper,
resource_tracker, graph)
progress_bar.update(vertex.n_atoms)
progress_bar.end()
partition_algorithm_utilities.generate_sub_edges(
subgraph, graph_mapper, graph)
results = dict()
results['partitioned_graph'] = subgraph
results['graph_mapper'] = graph_mapper
return results
def setUp(self):
########################################################################
# Setting up vertices, edges and graph #
########################################################################
self.vert1 = TestVertex(100, "New AbstractConstrainedTestVertex 1")
self.vert2 = TestVertex(5, "New AbstractConstrainedTestVertex 2")
self.vert3 = TestVertex(3, "New AbstractConstrainedTestVertex 3")
self.edge1 = MultiCastPartitionableEdge(self.vert1, self.vert2,
"First edge")
self.edge2 = MultiCastPartitionableEdge(self.vert2, self.vert1,
"Second edge")
self.edge3 = MultiCastPartitionableEdge(self.vert1, self.vert3,
"Third edge")
self.verts = [self.vert1, self.vert2, self.vert3]
self.edges = [self.edge1, self.edge2, self.edge3]
self.graph = PartitionableGraph("Graph", self.verts, self.edges)
########################################################################
# Setting up machine #
########################################################################
flops = 1000
(e, ne, n, w, sw, s) = range(6)
processors = list()
for i in range(18):
processors.append(Processor(i, flops))
_sdram = SDRAM(128 * (2**20))
ip = "192.168.240.253"
chips = list()
for x in range(10):
for y in range(10):
links = list()
links.append(Link(x, y, 0, (x + 1) % 10, y, n, n))
links.append(Link(x, y, 1, (x + 1) % 10, (y + 1) % 10, s, s))
links.append(Link(x, y, 2, x, (y + 1) % 10, n, n))
links.append(Link(x, y, 3, (x - 1) % 10, y, s, s))
links.append(Link(x, y, 4, (x - 1) % 10, (y - 1) % 10, n, n))
links.append(Link(x, y, 5, x, (y - 1) % 10, s, s))
r = Router(links, False, 100, 1024)
chips.append(Chip(x, y, processors, r, _sdram, 0, 0, ip))
self.machine = Machine(chips)
########################################################################
# Setting up subgraph and graph_mapper #
########################################################################
self.subvertices = list()
self.subvertex1 = PartitionedVertex(
0, 1, self.vert1.get_resources_used_by_atoms(0, 1, []),
"First subvertex")
self.subvertex2 = PartitionedVertex(
1, 5, get_resources_used_by_atoms(1, 5, []), "Second subvertex")
self.subvertex3 = PartitionedVertex(
5, 10, get_resources_used_by_atoms(5, 10, []), "Third subvertex")
self.subvertex4 = PartitionedVertex(
10, 100, get_resources_used_by_atoms(10, 100, []),
"Fourth subvertex")
self.subvertices.append(self.subvertex1)
self.subvertices.append(self.subvertex2)
self.subvertices.append(self.subvertex3)
self.subvertices.append(self.subvertex4)
self.subedges = list()
self.subgraph = PartitionedGraph("Subgraph", self.subvertices,
self.subedges)
self.graph_mapper = GraphMapper()
self.graph_mapper.add_subvertices(self.subvertices)
def __call__(self, graph, machine):
""" Partition a partitionable_graph so that each subvertex will fit\
on a processor within the machine
:param graph: The partitionable_graph to partition
:type graph:\
:py:class:`pacman.model.graph.partitionable_graph.PartitionableGraph`
:param machine: The machine with respect to which to partition the\
partitionable_graph
:type machine: :py:class:`spinn_machine.machine.Machine`
:return: A partitioned_graph of partitioned vertices and partitioned\
edges
:rtype:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:raise pacman.exceptions.PacmanPartitionException: If something\
goes wrong with the partitioning
"""
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=graph.vertices,
abstract_constraint_type=AbstractPartitionerConstraint,
supported_constraints=[PartitionerMaximumSizeConstraint,
PartitionerSameSizeAsVertexConstraint])
# Load the vertices and create the subgraph to fill
vertices = graph.vertices
subgraph = PartitionedGraph(
label="partitioned graph for {}".format(graph.label))
graph_mapper = GraphMapper(graph.label, subgraph.label)
# sort out vertex's by constraints
vertices = utility_calls.sort_objects_by_constraint_authority(vertices)
# Set up the progress
n_atoms = 0
for vertex in vertices:
n_atoms += vertex.n_atoms
progress_bar = ProgressBar(n_atoms, "Partitioning graph vertices")
resource_tracker = ResourceTracker(machine)
# Partition one vertex at a time
for vertex in vertices:
# check that the vertex hasn't already been partitioned
subverts_from_vertex = \
graph_mapper.get_subvertices_from_vertex(vertex)
# if not, partition
if subverts_from_vertex is None:
self._partition_vertex(
vertex, subgraph, graph_mapper, resource_tracker, graph)
progress_bar.update(vertex.n_atoms)
progress_bar.end()
partition_algorithm_utilities.generate_sub_edges(
subgraph, graph_mapper, graph)
results = dict()
results['partitioned_graph'] = subgraph
results['graph_mapper'] = graph_mapper
results['nChips'] = len(resource_tracker.keys)
return results
