class TestMLP_network():
def __init__(self, mlp_network):
self.net = mlp_network #MLP_network(18,32,64,32,2)
self.map_strategy = MapStrategyEyeriss
self.resource = Resource(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC),
data_regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.DATA), ),
dim_array=PhyDim2(16, 16),
size_gbuf=128 * 1024 // 2, # 128 kB
size_regf=512 // 2, # 512 B
)
self.cost = Cost(mac_op=1,
mem_hier=(200, 6, 2, 1),
noc_hop=0,
unit_static=0)
self.options = Option()
def test_eyeriss_isca16(self):
network = self.net
batch_size = 16
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
self.map_strategy)
tops, cache_stats = nnd.schedule_search(self.options)
if not tops:
sys.stderr.write("No valid dataflow found!")
return None
dfsch = tops[0]
## Write results.
res_map = OrderedDict()
res_map['net'] = "MLP_L"
res_map['batch'] = batch_size
res_map['resource'] = self.resource._asdict()
res_map['cost'] = self.cost._asdict()
res_map['options'] = self.options._asdict()
res_map['cache_stats'] = cache_stats
stats = stats_dict(dfsch, self.cost)
for key, val in stats.items():
res_map[key] = val
return res_map
def do_scheduling(args):
'''
Get optimal scheduling for given problem. Return a result schedule.
'''
## Network.
network = import_network(args.net)
batch_size = args.batch
## Resource.
dim_nodes = PhyDim2(*args.nodes)
dim_array = PhyDim2(*args.array)
# Sizes of gbuf and regf are in words.
word = (args.word + 7) / 8
size_gbuf = args.gbuf / word
size_regf = args.regf / word
array_bus_width = args.bus_width // args.word
if not array_bus_width:
array_bus_width = float('inf')
dram_bandwidth = args.dram_bw / word
proc_region = NodeRegion(dim=dim_nodes,
origin=PhyDim2(0, 0),
type=NodeRegion.PROC)
if args.mem_type == '2D':
# Memory nodes are on two sides.
data_region = NodeRegion(dim=PhyDim2(2, 2),
origin=PhyDim2(0, 0),
dist=dim_nodes - PhyDim2(1, 1),
type=NodeRegion.DRAM)
assert data_region.rel2abs(PhyDim2(1, 1)) + PhyDim2(1, 1) \
== proc_region.dim
elif args.mem_type == '3D':
# Memory nodes are on the top.
data_region = NodeRegion(dim=dim_nodes,
origin=PhyDim2(0, 0),
type=NodeRegion.DRAM)
resource = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=size_gbuf,
size_regf=size_regf,
array_bus_width=array_bus_width,
dram_bandwidth=dram_bandwidth,
no_time_mux=False)
## Cost.
hier_cost = [0] * me.NUM
hier_cost[me.DRAM] = args.hier_cost[0]
hier_cost[me.GBUF] = args.hier_cost[1]
hier_cost[me.ITCN] = args.hier_cost[2]
hier_cost[me.REGF] = args.hier_cost[3]
cost = Cost(mac_op=args.op_cost,
mem_hier=tuple(hier_cost),
noc_hop=args.hop_cost,
idl_unit=args.unit_idle_cost)
## Options.
bypass = [True] * de.NUM
bypass[de.IFM] = 'i' not in args.disable_bypass
bypass[de.OFM] = 'o' not in args.disable_bypass
bypass[de.FIL] = 'f' not in args.disable_bypass
options = Option(
sw_gbuf_bypass=tuple(bypass),
sw_solve_loopblocking=args.solve_loopblocking,
hw_access_forwarding=args.enable_access_forwarding,
hw_gbuf_sharing=args.enable_gbuf_sharing,
hw_gbuf_save_writeback=args.enable_save_writeback,
partition_hybrid=args.hybrid_partition,
partition_batch=args.batch_partition,
partition_ifmaps=args.ifmaps_partition,
partition_interlayer=args.interlayer_partition,
layer_pipeline_time_ovhd=args.layer_pipeline_time_overhead,
layer_pipeline_max_degree=args.layer_pipeline_max_degree,
layer_pipeline_opt=not args.disable_interlayer_opt,
opt_goal=args.goal.lower(),
ntops=args.top,
nprocesses=args.processes,
verbose=args.verbose)
## Search schedules.
nnd = NNDataflow(network, batch_size, resource, cost, MapStrategyEyeriss)
tbeg = time.time()
tops, cache_stats = nnd.schedule_search(options)
tend = time.time()
telapsed = tend - tbeg
if not tops:
sys.stderr.write('No valid dataflow found.\n')
return None
top = tops[0]
## Write results.
res_map = OrderedDict()
res_map['version'] = get_version(with_local=True)
res_map['net'] = args.net
res_map['batch'] = args.batch
res_map['resource'] = resource._asdict()
res_map['cost'] = cost._asdict()
res_map['options'] = options._asdict()
res_map['cache_stats'] = cache_stats
res_map['elapsed'] = telapsed
stats = stats_dict(top, cost)
for key, val in stats.items():
res_map[key] = val
return res_map
class TestScheduling(unittest.TestCase):
''' Tests for Scheduling module. '''
def setUp(self):
self.layers = {}
self.layers['BASE'] = ConvLayer(8, 16, 28, 3)
self.layers['POOL'] = PoolingLayer(16, 28, 2)
self.layers['LR'] = LocalRegionLayer(16, 28, nreg=3, sreg=1)
self.batch_size = 4
self.cost = Cost(mac_op=1,
mem_hier=(200, 6, 2, 1),
noc_hop=50,
idl_unit=50)
self.none_cstr = SchedulingConstraint()
self.cstr = SchedulingConstraint(topofm=1, topbat=self.batch_size)
self.resource = Resource(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.PROC),
dram_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 1),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 1),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 1),
type=NodeRegion.DRAM),
dim_array=PhyDim2(16, 16),
size_gbuf=65536,
size_regf=64,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
self.options = Option(partition_hybrid=True,
partition_batch=True,
partition_ifmaps=True,
ntops=10)
self.ifmap_layouts = {}
part = PartitionScheme(order=(pe.INPP, pe.BATP, pe.OUTP, pe.OFMP),
pdims=((1, 2), (2, 1), (1, 2), (2, 1)))
for wlkey in self.layers:
input_layer = self.layers[wlkey].input_layer()
self.ifmap_layouts[wlkey] = DataLayout(
frngs=(FmapRange((0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=input_layer.nofm,
h=input_layer.hofm,
w=input_layer.wofm)), ),
regions=(self.resource.src_data_region, ),
parts=(part.projection(self.resource.src_data_region,
appl2frng=True), ))
self.sched_seq = (2, 0, 1)
def test_valid_args(self):
''' Valid arguments for constructor. '''
schd = Scheduling(self.layers['BASE'], self.batch_size, self.cost,
MapStrategyEyeriss)
self.assertEqual(schd.layer, self.layers['BASE'])
self.assertEqual(schd.batch_size, self.batch_size)
self.assertEqual(schd.cost, self.cost)
self.assertEqual(schd.map_strategy_class, MapStrategyEyeriss)
def test_invalid_layer(self):
''' Invalid layer argument. '''
with self.assertRaisesRegexp(TypeError, 'Scheduling: .*layer.*'):
_ = Scheduling((64, 128, 28, 3), self.batch_size, self.cost,
MapStrategyEyeriss)
def test_invalid_cost(self):
''' Invalid cost argument. '''
with self.assertRaisesRegexp(TypeError, 'Scheduling: .*cost.*'):
_ = Scheduling(self.layers['BASE'], self.batch_size,
tuple(self.cost), MapStrategyEyeriss)
def test_invalid_map_strategy(self):
''' Invalid cost argument. '''
class _DummyClass(object): # pylint: disable=too-few-public-methods
pass
with self.assertRaisesRegexp(TypeError,
'Scheduling: .*map_strategy_class.*'):
_ = Scheduling(self.layers['BASE'], self.batch_size, self.cost,
_DummyClass)
def test_schedule_search(self):
''' Schedule search. '''
for wlkey in self.layers:
layer = self.layers[wlkey]
ifmap_layout = self.ifmap_layouts[wlkey]
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
condition = SchedulingCondition(resource=self.resource,
constraint=self.cstr,
ifmap_layout=ifmap_layout,
sched_seq=self.sched_seq)
res = schd.schedule_search(condition, self.options)
# Top N.
self.assertLessEqual(len(res), self.options.ntops)
self.assertTrue(all(isinstance(r, SchedulingResult) for r in res))
for idx in range(len(res) - 1):
self.assertLessEqual(res[idx].total_cost,
res[idx + 1].total_cost)
# Combination of loop blocking and partitioning.
for r in res:
self.assertAlmostEqual(
r.total_cost,
r.scheme['cost_op'] + r.scheme['cost_access'] +
r.scheme['cost_noc'] + r.scheme['cost_static'])
self.assertEqual(r.total_ops, layer.total_ops(self.batch_size))
self.assertSequenceEqual(r.scheme['total_nhops'], [
nh * f for nh, f in zip(r.scheme['unit_nhops'],
r.scheme['fetch'][0])
])
self.assertEqual(r.num_nodes,
self.resource.proc_region.dim.size())
# Constraint.
for r in res:
self.assertEqual(r.scheme['to'][0], 1)
# Ofmap layout.
for r in res:
self.assertEqual(r.ofmap_layout.complete_fmap_range().size(),
layer.total_ofmap_size(self.batch_size))
# Sequence number.
for r in res:
self.assertTupleEqual(r.sched_seq, condition.sched_seq)
def test_schedule_search_ilayout(self):
''' Invalid ifmap_layout. '''
layer = self.layers['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
# Shift ifmap out of memory region.
condition = SchedulingCondition(
resource=self.resource,
constraint=self.none_cstr,
ifmap_layout=self.ifmap_layouts['BASE']._replace(regions=tuple(
r._replace(origin=PhyDim2(-10, -10))
for r in self.ifmap_layouts['BASE'].regions)),
sched_seq=self.sched_seq)
with self.assertRaisesRegexp(ValueError, 'Scheduling: .*ifmap.*'):
_ = schd.schedule_search(condition, self.options)
# Not match layer.
condition = SchedulingCondition(
resource=self.resource,
constraint=self.none_cstr,
ifmap_layout=self.ifmap_layouts['POOL'],
sched_seq=self.sched_seq)
with self.assertRaisesRegexp(ValueError, 'Scheduling: .*ifmap.*'):
_ = schd.schedule_search(condition, self.options)
def test_schedule_search_nolbs(self):
''' Schedule search with no lbs. '''
layer = self.layers['BASE']
ifmap_layout = self.ifmap_layouts['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
condition = SchedulingCondition(
resource=self.resource._replace(size_regf=0),
constraint=self.none_cstr,
ifmap_layout=ifmap_layout,
sched_seq=self.sched_seq)
res = schd.schedule_search(condition, self.options)
self.assertFalse(res)
def test_pernode_sched_cache(self):
''' Per-node scheduling cache. '''
# pylint: disable=no-member
Scheduling.schedule_search_per_node.cache_clear()
layer = self.layers['BASE']
ifmap_layout = self.ifmap_layouts['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
self.assertEqual(schd.schedule_search_per_node.cache_info().currsize,
0)
self.assertTupleEqual(schd.cache_stats(), (0, 0))
condition = SchedulingCondition(resource=self.resource,
constraint=self.cstr,
ifmap_layout=ifmap_layout,
sched_seq=self.sched_seq)
Scheduling.schedule_search.cache_clear()
_ = schd.schedule_search(condition, self.options)
h, m = schd.cache_stats()
self.assertEqual(schd.schedule_search_per_node.cache_info().currsize,
m)
self.assertEqual(h, 0)
n = m
Scheduling.schedule_search.cache_clear()
_ = schd.schedule_search(condition, self.options)
self.assertEqual(schd.schedule_search_per_node.cache_info().currsize,
n)
self.assertTupleEqual(schd.cache_stats(), (n, n))
def test_pernode_sched_cache_key(self):
''' Per-node scheduling cache key must be hash-able. '''
# pylint: disable=no-member
Scheduling.schedule_search.cache_clear()
Scheduling.schedule_search_per_node.cache_clear()
layer = self.layers['BASE']
ifmap_layout = self.ifmap_layouts['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
condition = SchedulingCondition(resource=self.resource,
constraint=self.cstr,
ifmap_layout=ifmap_layout,
sched_seq=self.sched_seq)
_ = schd.schedule_search(condition, self.options)
h, m = schd.cache_stats()
self.assertEqual(h, 0)
# Make another instance.
rsrc = Resource(**self.resource._asdict())
cstr = self.cstr
opts = Option(**self.options._asdict())
self.assertNotEqual(id(rsrc), id(self.resource))
self.assertNotEqual(id(opts), id(self.options))
part = PartitionScheme(order=(pe.BATP, pe.INPP, pe.OUTP, pe.OFMP),
pdims=((2, 4), (2, 1), (1, 1), (1, 1)))
_ = schd.schedule_search_per_node(part, rsrc, cstr, opts)
h2, m2 = schd.cache_stats()
self.assertEqual(h2, h + 1)
self.assertEqual(m2, m)
class TestScheduling(unittest.TestCase):
''' Tests for Scheduling module. '''
def setUp(self):
self.layers = {}
self.layers['BASE'] = ConvLayer(8, 16, 28, 3)
self.layers['POOL'] = PoolingLayer(16, 28, 2)
self.layers['LR'] = LocalRegionLayer(16, 28, nreg=3, sreg=1)
self.batch_size = 4
self.cost = Cost(mac_op=1, mem_hier=(200, 6, 2, 1),
noc_hop=50, unit_static=50)
self.resource = Resource(
proc_region=NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(4, 4),
type=NodeRegion.PROC),
data_regions=(NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(4, 1),
type=NodeRegion.DATA),),
dim_array=PhyDim2(16, 16), size_gbuf=65536, size_regf=64)
self.options = Option(partition_hybrid=True, partition_batch=True,
partition_ifmaps=True, ntops=10)
self.ifmap_layouts = {}
part = PartitionScheme(order=(pe.INPP, pe.BATP, pe.OUTP, pe.OFMP),
pdims=((1, 2), (2, 1), (1, 2), (2, 1)))
for wlkey in self.layers:
self.ifmap_layouts[wlkey] = partition.get_ofmap_layout(
self.layers[wlkey].input_layer(), self.batch_size, part,
self.resource.src_data_region())
def test_valid_args(self):
''' Valid arguments for constructor. '''
schd = Scheduling(self.layers['BASE'], self.batch_size, self.cost,
MapStrategyEyeriss)
self.assertEqual(schd.layer, self.layers['BASE'])
self.assertEqual(schd.batch_size, self.batch_size)
self.assertEqual(schd.cost, self.cost)
self.assertEqual(schd.map_strategy_class, MapStrategyEyeriss)
def test_invalid_layer(self):
''' Invalid layer argument. '''
with self.assertRaisesRegexp(TypeError, 'Scheduling: .*layer.*'):
_ = Scheduling((64, 128, 28, 3), self.batch_size, self.cost,
MapStrategyEyeriss)
def test_invalid_cost(self):
''' Invalid cost argument. '''
with self.assertRaisesRegexp(TypeError, 'Scheduling: .*cost.*'):
_ = Scheduling(self.layers['BASE'], self.batch_size,
tuple(self.cost), MapStrategyEyeriss)
def test_invalid_map_strategy(self):
''' Invalid cost argument. '''
class _DummyClass(object): # pylint: disable=too-few-public-methods
pass
with self.assertRaisesRegexp(TypeError,
'Scheduling: .*map_strategy_class.*'):
_ = Scheduling(self.layers['BASE'], self.batch_size, self.cost,
_DummyClass)
def test_schedule_search(self):
''' Schedule search. '''
for wlkey in self.layers:
layer = self.layers[wlkey]
ifmap_layout = self.ifmap_layouts[wlkey]
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
condition = SchedulingCondition(resource=self.resource,
ifmap_layout=ifmap_layout)
res = schd.schedule_search(condition, self.options)
# Top N.
self.assertLessEqual(len(res), self.options.ntops)
self.assertTrue(all(isinstance(r, SchedulingResult) for r in res))
for idx in range(len(res) - 1):
self.assertLessEqual(res[idx].total_cost,
res[idx + 1].total_cost)
# Combination of loop blocking and partitioning.
for r in res:
self.assertEqual(r.total_cost,
r.dict_loop['cost'] + r.dict_part['cost'])
self.assertEqual(r.dict_loop['ops'],
layer.total_ops(self.batch_size))
self.assertSequenceEqual(r.dict_part['total_nhops'],
[nh * f for nh, f
in zip(r.dict_part['unit_nhops'],
r.dict_loop['fetch'][0])])
self.assertEqual(r.dict_part['num_nodes'],
self.resource.proc_region.dim.size())
# Ofmap layout.
for r in res:
self.assertEqual(r.ofmap_layout.frmap.complete_fmap_range()
.size(),
layer.total_ofmap_size(self.batch_size))
def test_schedule_search_ilayout(self):
''' Invalid ifmap_layout. '''
layer = self.layers['BASE']
ifmap_layout = self.ifmap_layouts['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
# Shift ifmap out of memory region.
condition = SchedulingCondition(
resource=self.resource,
ifmap_layout=ifmap_layout.view(PhyDim2(1, 1)))
with self.assertRaisesRegexp(ValueError, 'Scheduling: .*ifmap.*'):
_ = schd.schedule_search(condition, self.options)
# Not match layer.
condition = SchedulingCondition(
resource=self.resource,
ifmap_layout=self.ifmap_layouts['POOL'])
with self.assertRaisesRegexp(ValueError, 'Scheduling: .*ifmap.*'):
_ = schd.schedule_search(condition, self.options)
def test_pernode_sched_cache(self):
''' Per-node scheduling cache. '''
layer = self.layers['BASE']
ifmap_layout = self.ifmap_layouts['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
self.assertEqual(len(schd.pernode_sched_cache), 0)
self.assertTupleEqual(schd.cache_stats(), (0, 0))
condition = SchedulingCondition(resource=self.resource,
ifmap_layout=ifmap_layout)
_ = schd.schedule_search(condition, self.options)
h, m = schd.cache_stats()
self.assertEqual(len(schd.pernode_sched_cache), m)
self.assertEqual(h, 0)
n = m
_ = schd.schedule_search(condition, self.options)
self.assertEqual(len(schd.pernode_sched_cache), n)
self.assertTupleEqual(schd.cache_stats(), (n, n))
def test_pernode_sched_cache_key(self):
''' Per-node scheduling cache key must be hash-able. '''
layer = self.layers['BASE']
ifmap_layout = self.ifmap_layouts['BASE']
schd = Scheduling(layer, self.batch_size, self.cost,
MapStrategyEyeriss)
condition = SchedulingCondition(resource=self.resource,
ifmap_layout=ifmap_layout)
_ = schd.schedule_search(condition, self.options)
h, m = schd.cache_stats()
self.assertEqual(h, 0)
# Make another instance.
rsrc = Resource(**self.resource._asdict())
opts = Option(**self.options._asdict())
self.assertNotEqual(id(rsrc), id(self.resource))
self.assertNotEqual(id(opts), id(self.options))
part = PartitionScheme(order=(pe.BATP, pe.INPP, pe.OUTP, pe.OFMP),
pdims=((2, 2), (2, 2), (1, 1), (1, 1)))
_ = schd.schedule_search_per_node(part, rsrc, opts)
h2, m2 = schd.cache_stats()
self.assertEqual(h2, h + 1)
self.assertEqual(m2, m)
def do_scheduling(args):
'''
Get optimal scheduling for given problem. Return a result schedule.
'''
## Network.
network = import_network(args.net)
batch_size = args.batch
## Resource.
dim_nodes = PhyDim2(*args.nodes)
dim_array = PhyDim2(*args.array)
# Sizes of gbuf and regf are in words.
word = (args.word + 7) / 8
size_gbuf = args.gbuf / word
size_regf = args.regf / word
proc_region = NodeRegion(dim=dim_nodes,
origin=PhyDim2(0, 0),
type=NodeRegion.PROC)
if args.mem_type == '2D':
# Memory nodes are on two sides.
data_regions = (NodeRegion(dim=PhyDim2(h=dim_nodes.h, w=1),
origin=PhyDim2(h=0, w=0),
type=NodeRegion.DATA),
NodeRegion(dim=PhyDim2(h=dim_nodes.h, w=1),
origin=PhyDim2(h=0, w=dim_nodes.w - 1),
type=NodeRegion.DATA))
elif args.mem_type == '3D':
# All nodes have memory.
data_regions = (NodeRegion(dim=dim_nodes,
origin=PhyDim2(0, 0),
type=NodeRegion.DATA), )
resource = Resource(proc_region=proc_region,
data_regions=data_regions,
dim_array=dim_array,
size_gbuf=size_gbuf,
size_regf=size_regf)
## Cost.
hier_cost = [0] * me.NUM
hier_cost[me.DRAM] = args.hier_cost[0]
hier_cost[me.GBUF] = args.hier_cost[1]
hier_cost[me.ITCN] = args.hier_cost[2]
hier_cost[me.REGF] = args.hier_cost[3]
cost = Cost(mac_op=args.op_cost,
mem_hier=tuple(hier_cost),
noc_hop=args.hop_cost,
unit_static=args.unit_static_cost)
## Options.
bypass = [True] * de.NUM
bypass[de.IFM] = 'i' not in args.disable_bypass
bypass[de.OFM] = 'o' not in args.disable_bypass
bypass[de.FIL] = 'f' not in args.disable_bypass
options = Option(sw_gbuf_bypass=tuple(bypass),
sw_solve_loopblocking=args.solve_loopblocking,
partition_hybrid=args.hybrid_partition,
partition_batch=args.batch_partition,
partition_ifmaps=args.ifmaps_partition,
ntops=args.top,
nprocesses=args.processes,
verbose=args.verbose)
## Search schedules.
nnd = NNDataflow(network, batch_size, resource, cost, MapStrategyEyeriss)
tops, cache_stats = nnd.schedule_search(options)
if not tops:
sys.stderr.write('No valid dataflow found.\n')
return None
top = tops[0]
## Write results.
res_map = OrderedDict()
res_map['version'] = get_version(with_local=True)
res_map['net'] = args.net
res_map['batch'] = args.batch
res_map['resource'] = resource._asdict()
res_map['cost'] = cost._asdict()
res_map['options'] = options._asdict()
res_map['cache_stats'] = cache_stats
stats = stats_dict(top, cost)
for key, val in stats.items():
res_map[key] = val
return res_map
