class TestNNDataflowScheme(unittest.TestCase):
''' Tests for NNDataflowScheme. '''
def setUp(self):
self.network = Network('test_net')
self.network.set_input(InputLayer(3, 224))
self.network.add('c1', ConvLayer(3, 64, 224, 3))
self.network.add('p1', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('p2', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('f1', FCLayer(64, 1000, 7), prevs=['p1', 'p2'])
self.batch_size = 4
self.input_layout = partition.get_ofmap_layout(
self.network.input_layer(), self.batch_size,
PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM),
NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(2, 1),
type=NodeRegion.DATA))
self.c1res = SchedulingResult(
dict_loop=OrderedDict([('cost', 1.), ('time', 2.), ('ops', 4.),
('access', [[7, 8, 9]] * me.NUM),
]),
dict_part=OrderedDict([('cost', 0.5), ('total_nhops', [4, 5, 6]),
('num_nodes', 4),
]),
ofmap_layout=partition.get_ofmap_layout(
self.network['c1'], self.batch_size,
PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM),
NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(1, 2),
type=NodeRegion.DATA)))
self.pres = SchedulingResult(
dict_loop=OrderedDict([('cost', 0.1), ('time', 0.05), ('ops', 0.1),
('access', [[.7, .8, .9]] * me.NUM),
]),
dict_part=OrderedDict([('cost', 0.5), ('total_nhops', [.4, .5, .6]),
('num_nodes', 2),
]),
ofmap_layout=partition.get_ofmap_layout(
self.network['p1'], self.batch_size,
PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM),
NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(1, 2),
type=NodeRegion.DATA)))
self.dtfl = NNDataflowScheme(self.network, self.input_layout)
self.dtfl['c1'] = self.c1res
self.dtfl['p1'] = self.pres
self.dtfl['p2'] = self.pres
def test_init(self):
''' Initial. '''
df = NNDataflowScheme(self.network, self.input_layout)
self.assertEqual(df.network, self.network)
self.assertEqual(df.input_layout, self.input_layout)
self.assertEqual(df.total_cost, 0)
self.assertEqual(df.total_time, 0)
self.assertFalse(df.res_dict)
self.assertFalse(df)
self.assertEqual(df.total_ops, 0)
self.assertSequenceEqual(df.total_accesses, [0] * me.NUM)
self.assertEqual(df.total_noc_hops, 0)
def test_init_invalid_network(self):
''' Invalid network. '''
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*network*'):
_ = NNDataflowScheme(self.network['c1'], self.input_layout)
def test_init_invalid_input_layout(self):
''' Invalid input_layout. '''
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*input_layout*'):
_ = NNDataflowScheme(self.network, self.input_layout.frmap)
def test_setgetitem(self):
''' __set/getitem__. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
self.assertEqual(df['c1'], self.c1res)
def test_getitem_not_in(self):
''' __getitem__ not in. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaises(KeyError):
_ = df['c1']
def test_setitem_not_in_network(self):
''' __setitem__ not in network. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*cc.*'):
df['cc'] = self.c1res
def test_setitem_invalid_value(self):
''' __setitem__ invalid value. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*SchedulingResult*'):
df['c1'] = self.c1res.dict_loop
def test_setitem_already_exists(self):
''' __setitem__ already exists. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*c1*'):
df['c1'] = self.c1res
def test_setitem_prev_not_in(self):
''' __setitem__ already exists. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*p1*'):
df['p1'] = self.pres
def test_delitem(self):
''' __delitem__. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*'):
del df['c1']
def test_iter_len(self):
''' __iter__ and __len__. '''
self.assertEqual(len(self.dtfl), 3)
lst = [l for l in self.dtfl]
self.assertIn('c1', lst)
self.assertIn('p1', lst)
self.assertIn('p2', lst)
self.assertNotIn('f1', lst)
def test_copy(self):
''' copy. '''
df = self.dtfl
df2 = df.copy()
self.assertAlmostEqual(df.total_cost, df2.total_cost)
self.assertAlmostEqual(df.total_time, df2.total_time)
self.assertDictEqual(df.res_dict, df2.res_dict)
# Shallow copy.
for layer_name in df:
self.assertEqual(id(df[layer_name]), id(df2[layer_name]))
def test_properties(self):
''' Property accessors. '''
self.assertAlmostEqual(self.dtfl.total_cost, 1.5 + 0.6 * 2)
self.assertAlmostEqual(self.dtfl.total_time, 2 + 0.05 * 2)
self.assertAlmostEqual(self.dtfl.total_ops, 4 + 0.1 * 2)
for a in self.dtfl.total_accesses:
self.assertAlmostEqual(a, (7 + 8 + 9) + (.7 + .8 + .9) * 2)
self.assertAlmostEqual(self.dtfl.total_noc_hops,
(4 + 5 + 6) + (.4 + .5 + .6) * 2)
self.assertAlmostEqual(self.dtfl.total_node_time, 2 * 4 + 0.05 * 2 * 2)
def test_stats_active_node_pes(self):
''' Per-layer stats: active node PEs. '''
stats = self.dtfl.perlayer_stats('active_node_pes')
self.assertEqual(len(stats), len(self.dtfl))
self.assertAlmostEqual(stats['c1'], 0.5)
self.assertAlmostEqual(stats['p1'], 1)
self.assertAlmostEqual(stats['p2'], 1)
def test_stats_total_dram_bw(self):
''' Per-layer stats: total DRAM bandwidth. '''
stats = self.dtfl.perlayer_stats('total_dram_bandwidth')
self.assertEqual(len(stats), len(self.dtfl))
self.assertAlmostEqual(stats['c1'], (7 + 8 + 9) / 2.)
self.assertAlmostEqual(stats['p1'], (.7 + .8 + .9) / 0.05)
self.assertAlmostEqual(stats['p2'], (.7 + .8 + .9) / 0.05)
def test_stats_not_supported(self):
''' Per-layer stats: not supported. '''
with self.assertRaisesRegexp(AttributeError,
'NNDataflowScheme: .*not_supported.*'):
_ = self.dtfl.perlayer_stats('not_supported')
class TestNNDataflowScheme(unittest.TestCase):
''' Tests for NNDataflowScheme. '''
# pylint: disable=too-many-public-methods
# pylint: disable=too-many-public-methods
def setUp(self):
self.network = Network('test_net')
self.network.set_input_layer(InputLayer(3, 224))
self.network.add('c1', ConvLayer(3, 64, 224, 3))
self.network.add('p1', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('p2', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('f1', FCLayer(128, 1000, 7), prevs=['p1', 'p2'])
self.batch_size = 4
input_layer = self.network.input_layer()
self.input_layout = 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=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(2, 1),
type=NodeRegion.DRAM), ),
parts=(PartitionScheme(order=range(pe.NUM),
pdims=[(1, 1)] * pe.NUM), ))
c1_layer = self.network['c1']
self.c1res = SchedulingResult(
scheme=OrderedDict([
('cost', 1.5),
('time', 200.),
('ops', 4.),
('num_nodes', 4),
('cost_op', 0.5),
('cost_access', 1.),
('cost_noc', 0),
('cost_static', 0),
('proc_time', 200),
('bus_time', 0),
('dram_time', 0),
('access', [[7, 8, 9]] * me.NUM),
('remote_gbuf_access', [0] * 3),
('total_nhops', [4, 5, 6]),
('fetch', [[1, 1, 1], [2, 2, 2]]),
('ti', [2, 2, 3]),
('to', [1, 2, 3]),
('tb', [1, 2, 3]),
('tvals', [[2, 1, 1], [2, 2, 2], [3, 3, 3]]),
('orders', [range(3)] * 2),
]),
ofmap_layout=DataLayout(
frngs=(FmapRange(
(0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=c1_layer.nofm,
h=c1_layer.hofm,
w=c1_layer.wofm)), ),
regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 2),
type=NodeRegion.DRAM), ),
parts=(PartitionScheme(order=range(pe.NUM),
pdims=[(1, 1)] * pe.NUM), )),
sched_seq=(0, 0, 0))
p1_layer = self.network['p1']
self.p1res = SchedulingResult(
scheme=OrderedDict([
('cost', 0.6),
('time', 5),
('ops', 0.1),
('num_nodes', 2),
('cost_op', 0.1),
('cost_access', 0.5),
('cost_noc', 0),
('cost_static', 0),
('proc_time', 5),
('bus_time', 0),
('dram_time', 0),
('access', [[.7, .8, .9]] * me.NUM),
('remote_gbuf_access', [0] * 3),
('total_nhops', [.4, .5, .6]),
('fetch', [[1, 1, 1], [2, 2, 2]]),
('ti', [2, 2, 3]),
('to', [1, 2, 3]),
('tb', [1, 2, 3]),
('tvals', [[2, 1, 1], [2, 2, 2], [3, 3, 3]]),
('orders', [range(3)] * 2),
]),
ofmap_layout=DataLayout(
frngs=(FmapRange(
(0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=p1_layer.nofm,
h=p1_layer.hofm,
w=p1_layer.wofm)), ),
regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 2),
type=NodeRegion.DRAM), ),
parts=(PartitionScheme(order=range(pe.NUM),
pdims=[(1, 1)] * pe.NUM), )),
sched_seq=(0, 1, 0))
self.p2res = SchedulingResult(scheme=self.p1res.scheme,
ofmap_layout=self.p1res.ofmap_layout,
sched_seq=(0, 2, 0))
self.dtfl = NNDataflowScheme(self.network, self.input_layout)
self.dtfl['c1'] = self.c1res
self.dtfl['p1'] = self.p1res
self.dtfl['p2'] = self.p2res
def test_init(self):
''' Initial. '''
df = NNDataflowScheme(self.network, self.input_layout)
self.assertEqual(df.network, self.network)
self.assertEqual(df.input_layout, self.input_layout)
self.assertDictEqual(df.ext_layout_dict, {})
self.assertEqual(df.total_cost, 0)
self.assertEqual(df.total_time, 0)
self.assertFalse(df.res_dict)
self.assertFalse(df)
self.assertEqual(df.total_ops, 0)
self.assertSequenceEqual(df.total_accesses, [0] * me.NUM)
self.assertEqual(df.total_noc_hops, 0)
def test_init_ext(self):
''' Initial with external layers. '''
self.network.add_ext('e0', InputLayer(3, 224))
self.network.add_ext('e1', InputLayer(6, 224))
e0_layout = DataLayout(frngs=(FmapRange(
(0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=self.network['e0'].nofm,
h=self.network['e0'].hofm,
w=self.network['e0'].wofm)), ),
regions=self.input_layout.regions,
parts=self.input_layout.parts)
e1_layout = DataLayout(frngs=(FmapRange(
(0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=self.network['e1'].nofm,
h=self.network['e1'].hofm,
w=self.network['e1'].wofm)), ),
regions=self.input_layout.regions,
parts=self.input_layout.parts)
ext_layout_dict = {'e0': e0_layout, 'e1': e1_layout}
df = NNDataflowScheme(self.network, self.input_layout, ext_layout_dict)
self.assertIn('e0', df.ext_layout_dict)
self.assertIn('e1', df.ext_layout_dict)
self.assertEqual(df.ext_layout_dict['e0'], e0_layout)
self.assertEqual(df.ext_layout_dict['e1'], e1_layout)
def test_init_invalid_network(self):
''' Invalid network. '''
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*network*'):
_ = NNDataflowScheme(self.network['c1'], self.input_layout)
def test_init_invalid_input_layout(self):
''' Invalid input_layout. '''
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*input_layout*'):
_ = NNDataflowScheme(self.network, self.input_layout.frngs)
def test_init_invalid_eld_keys(self):
''' Invalid ext_layout_dict keys. '''
with self.assertRaisesRegexp(ValueError,
'NNDataflowScheme: .*ext_layout_dict*'):
_ = NNDataflowScheme(self.network, self.input_layout,
{'e0': self.input_layout})
self.network.add_ext('e0', InputLayer(3, 224))
with self.assertRaisesRegexp(ValueError,
'NNDataflowScheme: .*ext_layout_dict*'):
_ = NNDataflowScheme(self.network, self.input_layout)
def test_init_invalid_eld_type(self):
''' Invalid ext_layout_dict value type. '''
self.network.add_ext('e0', InputLayer(3, 224))
self.network.add_ext('e1', InputLayer(3, 224))
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*ext_layout*'):
_ = NNDataflowScheme(self.network, self.input_layout, {
'e0': self.input_layout,
'e1': self.input_layout.frngs
})
def test_setgetitem(self):
''' __set/getitem__. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
self.assertEqual(df['c1'], self.c1res)
def test_getitem_not_in(self):
''' __getitem__ not in. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaises(KeyError):
_ = df['c1']
def test_setitem_not_in_network(self):
''' __setitem__ not in network. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*cc.*'):
df['cc'] = self.c1res
def test_setitem_invalid_value(self):
''' __setitem__ invalid value. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(TypeError,
'NNDataflowScheme: .*SchedulingResult*'):
df['c1'] = self.c1res.scheme
def test_setitem_already_exists(self):
''' __setitem__ already exists. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*c1*'):
df['c1'] = self.c1res._replace(sched_seq=(1, 0, 0))
def test_setitem_prev_not_in(self):
''' __setitem__ previous not existing. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*p1*'):
df['p1'] = self.p1res
def test_setitem_prev_input_ext(self):
''' __setitem__ previous is input or external. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
self.assertAlmostEqual(df.total_cost, self.c1res.total_cost)
self.network.add_ext('e0', InputLayer(3, 224))
self.network.add('c2', self.network['c1'], prevs=('e0', ))
df = NNDataflowScheme(self.network, self.input_layout,
{'e0': self.input_layout})
df['c2'] = self.c1res
self.assertAlmostEqual(df.total_cost, self.c1res.total_cost)
def test_setitem_invalid_seg_idx(self):
''' __setitem__ invalid segment index. '''
df = NNDataflowScheme(self.network, self.input_layout)
with self.assertRaisesRegexp(ValueError,
'NNDataflowScheme: .*segment index*'):
df['c1'] = self.c1res._replace(sched_seq=(1, 0, 0))
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
df['p1'] = self.p1res._replace(sched_seq=(1, 0, 0))
with self.assertRaisesRegexp(ValueError,
'NNDataflowScheme: .*segment index*'):
df['p2'] = self.p2res._replace(sched_seq=(0, 0, 0))
def test_delitem(self):
''' __delitem__. '''
df = NNDataflowScheme(self.network, self.input_layout)
df['c1'] = self.c1res
with self.assertRaisesRegexp(KeyError, 'NNDataflowScheme: .*'):
del df['c1']
def test_iter_len(self):
''' __iter__ and __len__. '''
self.assertEqual(len(self.dtfl), 3)
lst = [l for l in self.dtfl]
self.assertIn('c1', lst)
self.assertIn('p1', lst)
self.assertIn('p2', lst)
self.assertNotIn('f1', lst)
def test_copy(self):
''' copy. '''
df = self.dtfl
df2 = df.copy()
self.assertAlmostEqual(df.total_cost, df2.total_cost)
self.assertAlmostEqual(df.total_time, df2.total_time)
self.assertDictEqual(df.res_dict, df2.res_dict)
# Shallow copy.
for layer_name in df:
self.assertEqual(id(df[layer_name]), id(df2[layer_name]))
def test_copy_ext(self):
''' copy external layers. '''
self.network.add_ext('e0', self.network.input_layer())
self.network.add_ext('e1', self.network.input_layer())
df1 = NNDataflowScheme(self.network, self.input_layout, {
'e0': self.input_layout,
'e1': self.input_layout
})
df1['c1'] = self.c1res
df1['p1'] = self.p1res
df1['p2'] = self.p2res
df2 = df1.copy()
self.assertAlmostEqual(df1.total_cost, df2.total_cost)
self.assertAlmostEqual(df1.total_time, df2.total_time)
self.assertDictEqual(df1.res_dict, df2.res_dict)
self.assertDictEqual(df1.ext_layout_dict, df2.ext_layout_dict)
def test_fmap_layout(self):
''' fmap_layout. '''
flayout = self.dtfl.fmap_layout(('c1', ))
frng = flayout.complete_fmap_range()
self.assertTrue(flayout.is_in(self.c1res.ofmap_layout.regions[0]))
self.assertEqual(frng, self.c1res.ofmap_layout.frngs[0])
flayout = self.dtfl.fmap_layout((None, ))
frng = flayout.complete_fmap_range()
self.assertTrue(flayout.is_in(self.input_layout.regions[0]))
self.assertEqual(frng, self.input_layout.frngs[0])
flayout = self.dtfl.fmap_layout(('p1', 'p2'))
frng = flayout.complete_fmap_range()
self.assertEqual(frng.size('n'),
self.network['p1'].nofm + self.network['p2'].nofm)
flayout = self.dtfl.fmap_layout((None, 'c1'))
frng = flayout.complete_fmap_range()
self.assertEqual(
frng.size('n'),
self.network.input_layer().nofm + self.network['c1'].nofm)
def test_fmap_layout_ext(self):
''' fmap_layout external layers. '''
self.network.add_ext('e0', self.network.input_layer())
self.network.add_ext('e1', self.network.input_layer())
df = NNDataflowScheme(self.network, self.input_layout, {
'e0': self.input_layout,
'e1': self.input_layout
})
df['c1'] = self.c1res
df['p1'] = self.p1res
df['p2'] = self.p2res
flayout = df.fmap_layout(('e0', ))
self.assertEqual(flayout, self.input_layout)
flayout = df.fmap_layout(('e1', None))
self.assertTrue(flayout.is_in(self.input_layout.regions[0]))
frng = flayout.complete_fmap_range()
self.assertEqual(
frng.size('n'),
self.network['e1'].nofm + self.network.input_layer().nofm)
def test_properties(self):
''' Property accessors. '''
self.assertAlmostEqual(self.dtfl.total_cost, 1.5 + 0.6 * 2)
self.assertAlmostEqual(self.dtfl.total_time, 200 + 5)
self.assertAlmostEqual(self.dtfl.total_ops, 4 + 0.1 * 2)
for a in self.dtfl.total_accesses:
self.assertAlmostEqual(a, (7 + 8 + 9) + (.7 + .8 + .9) * 2)
self.assertAlmostEqual(self.dtfl.total_noc_hops,
(4 + 5 + 6) + (.4 + .5 + .6) * 2)
def test_time_full_net_single_seg(self):
''' time() when full network fits in a single segment. '''
dtfl = NNDataflowScheme(self.network, self.input_layout)
dtfl['c1'] = self.c1res
dtfl['p1'] = self.p1res._replace(sched_seq=(0, 1, 0))
dtfl['p2'] = self.p2res._replace(sched_seq=(0, 2, 0))
dtfl['f1'] = self.c1res._replace(sched_seq=(0, 3, 0))
self.assertEqual(dtfl.total_time, 200)
def test_static_cost_adjust(self):
''' Adjust static cost portion. '''
# Add static cost.
idl_unit_cost = 1e-3
c1scheme = self.c1res.scheme
c1static = c1scheme['time'] * idl_unit_cost
c1scheme['cost_static'] += c1static
c1scheme['cost_access'] -= c1static
p1scheme = self.p1res.scheme
p1static = p1scheme['time'] * idl_unit_cost
p1scheme['cost_static'] += p1static
p1scheme['cost_access'] -= p1static
# No adjust.
dtfl = NNDataflowScheme(self.network, self.input_layout)
dtfl['c1'] = self.c1res._replace(scheme=c1scheme)
dtfl['p1'] = self.p1res._replace(scheme=p1scheme, sched_seq=(1, 0, 0))
dtfl['p2'] = self.p2res._replace(scheme=p1scheme, sched_seq=(2, 0, 0))
dtfl['f1'] = self.c1res._replace(scheme=c1scheme, sched_seq=(3, 0, 0))
sum_cost = 1.5 + 0.6 + 0.6 + 1.5
sum_time = 200 + 5 + 5 + 200
self.assertAlmostEqual(dtfl.total_cost, sum_cost)
self.assertAlmostEqual(dtfl.total_time, sum_time)
# With adjust.
dtfl = NNDataflowScheme(self.network, self.input_layout)
dtfl['c1'] = self.c1res._replace(scheme=c1scheme)
dtfl['p1'] = self.p1res._replace(scheme=p1scheme, sched_seq=(0, 1, 0))
dtfl['p2'] = self.p2res._replace(scheme=p1scheme, sched_seq=(0, 2, 0))
dtfl['f1'] = self.c1res._replace(scheme=c1scheme, sched_seq=(1, 0, 0))
diff = (sum_time - dtfl.total_time) * idl_unit_cost
self.assertGreater(diff, 0)
self.assertAlmostEqual(dtfl.total_cost, sum_cost - diff)
# All in one segment.
dtfl = NNDataflowScheme(self.network, self.input_layout)
dtfl['c1'] = self.c1res._replace(scheme=c1scheme)
dtfl['p1'] = self.p1res._replace(scheme=p1scheme, sched_seq=(0, 1, 0))
dtfl['p2'] = self.p2res._replace(scheme=p1scheme, sched_seq=(0, 2, 0))
dtfl['f1'] = self.c1res._replace(scheme=c1scheme, sched_seq=(0, 3, 0))
diff = (sum_time - dtfl.total_time) * idl_unit_cost
self.assertGreater(diff, 0)
self.assertAlmostEqual(dtfl.total_cost, sum_cost - diff)
def test_segment_time_list(self):
''' segment_time_list(). '''
dtfl = NNDataflowScheme(self.network, self.input_layout)
dtfl['c1'] = self.c1res
dtfl['p1'] = self.p1res
dtfl['p2'] = self.p2res._replace(sched_seq=(1, 0, 0))
self.assertListEqual(dtfl.segment_time_list(), [205, 5])
def test_segment_dram_time_list(self):
''' segment_dram_time_list(). '''
c1_scheme = self.c1res.scheme.copy()
c1_scheme['dram_time'] = 180
p1_scheme = self.p1res.scheme.copy()
p1_scheme['dram_time'] = 5
p2_scheme = self.p2res.scheme.copy()
p2_scheme['dram_time'] = 10
dtfl = NNDataflowScheme(self.network, self.input_layout)
dtfl['c1'] = self.c1res._replace(scheme=c1_scheme)
dtfl['p1'] = self.p1res._replace(scheme=p1_scheme)
dtfl['p2'] = self.p2res._replace(sched_seq=(1, 0, 0), scheme=p2_scheme)
self.assertListEqual(dtfl.segment_dram_time_list(), [185, 10])
self.assertListEqual(dtfl.segment_time_list(), [205, 10])
def test_stats_active_node_pes(self):
''' Per-layer stats: active node PEs. '''
stats = self.dtfl.perlayer_stats('active_node_pes')
self.assertEqual(len(stats), len(self.dtfl))
self.assertAlmostEqual(stats['c1'], 0.005)
self.assertAlmostEqual(stats['p1'], 0.01)
self.assertAlmostEqual(stats['p2'], 0.01)
def test_stats_dram_bandwidth(self):
''' Per-layer stats: DRAM bandwidth. '''
stats = self.dtfl.perlayer_stats('dram_bandwidth')
self.assertEqual(len(stats), len(self.dtfl))
self.assertAlmostEqual(stats['c1'], (7. + 8. + 9.) / 200)
self.assertAlmostEqual(stats['p1'], (.7 + .8 + .9) / 5)
self.assertAlmostEqual(stats['p2'], (.7 + .8 + .9) / 5)
def test_stats_not_supported(self):
''' Per-layer stats: not supported. '''
with self.assertRaisesRegexp(AttributeError,
'NNDataflowScheme: .*not_supported.*'):
_ = self.dtfl.perlayer_stats('not_supported')