def test_opt_goal(self):
''' Optimization goal. '''
network = self.alex_net
batch_size = 8
resource = self.resource._replace(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(8, 8),
type=NodeRegion.PROC)
)
nnd = NNDataflow(network, batch_size, resource, self.cost,
self.map_strategy)
options_e = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
partition_hybrid=True,
partition_batch=True,
opt_goal='e',
ntops=16)
tops_e, _ = nnd.schedule_search(options_e)
self.assertTrue(tops_e)
options_d = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
partition_hybrid=True,
partition_batch=True,
opt_goal='d',
ntops=16)
tops_d, _ = nnd.schedule_search(options_d)
self.assertTrue(tops_d)
options_ed = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
partition_hybrid=True,
partition_batch=True,
opt_goal='ed',
ntops=16)
tops_ed, _ = nnd.schedule_search(options_ed)
self.assertTrue(tops_ed)
self.assertLess(tops_e[0].total_cost, tops_d[0].total_cost)
self.assertLess(tops_e[0].total_cost, tops_ed[0].total_cost)
self.assertLess(tops_d[0].total_time, tops_e[0].total_time)
self.assertLess(tops_d[0].total_time, tops_ed[0].total_time)
# Sum of the smallest ED may not be the smallest; allow for error.
self.assertLess(tops_ed[0].total_cost * tops_ed[0].total_time,
tops_e[0].total_cost * tops_e[0].total_time * 1.05)
self.assertLess(tops_ed[0].total_cost * tops_ed[0].total_time,
tops_d[0].total_cost * tops_d[0].total_time * 1.05)
def test_verbose(self):
''' Verbose mode. '''
network = self.alex_net
batch_size = 16
options = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
verbose=True)
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
self.map_strategy)
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = stdout = StringIO()
sys.stderr = stderr = StringIO()
tops, _ = nnd.schedule_search(options)
sys.stdout = old_stdout
sys.stderr = old_stderr
stdout_value = stdout.getvalue()
stderr_value = stderr.getvalue()
stdout.close()
stderr.close()
self.assertTrue(tops)
self.assertFalse(stdout_value)
for layer in network:
self.assertIn(layer, stderr_value)
def test_scheduling_failure(self):
''' Layer scheduling failure. '''
network = self.alex_net
batch_size = 16
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
MapStrategy)
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = stdout = StringIO()
sys.stderr = stderr = StringIO()
with self.assertRaises(NotImplementedError):
_ = nnd.schedule_search(self.options)
sys.stdout = old_stdout
sys.stderr = old_stderr
stdout_value = stdout.getvalue()
stderr_value = stderr.getvalue()
stdout.close()
stderr.close()
self.assertFalse(stdout_value)
self.assertIn('Failed', stderr_value)
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 test_pipelining(self):
''' Pipelining. '''
network = self.alex_net
batch_size = 1
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True)
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
def test_fast_forward_found(self):
''' Enter fast forward due to early found. '''
network = self.simple_net
batch_size = 1
# No time overhead limit.
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True,
layer_pipeline_time_ovhd=float('inf'))
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
def test_no_valid_dataflow(self):
''' No valid dataflow is found. '''
# Very small REGF.
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(1, 1),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dim_array=PhyDim2(16, 16),
size_gbuf=128 * 1024 // 2, # 128 kB
size_regf=2,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False,
)
nnd = NNDataflow(self.alex_net, 4, self.resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(self.options)
self.assertFalse(tops)
# With inter-layer pipelining.
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True)
tops, _ = nnd.schedule_search(options)
self.assertFalse(tops)
def test_ext_layer(self):
''' With external layers. '''
network = self.alex_net
network.add_ext('e0', InputLayer(4, 1))
network.add('l1', FCLayer(1000, 4))
network.add('l2', FCLayer(8, 4), prevs=('e0', 'l1'))
batch_size = 16
options = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True)
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
def test_fmap_fwd(self):
'''
Fmap forward with shared mem sources or both on/off-chip destinations.
'''
network = self.complex_net
batch_size = 16
# Multiple nodes for spatial pipelining.
resource = self.resource._replace(proc_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(8, 8), type=NodeRegion.PROC), )
# No time overhead limit.
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True,
layer_pipeline_time_ovhd=float('inf'))
nnd = NNDataflow(network, batch_size, resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
def test_no_valid_dataflow(self):
''' No valid dataflow is found. '''
# Very small REGF.
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=2,
)
nnd = NNDataflow(self.alex_net, 4, self.resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(self.options)
self.assertFalse(tops)
def test_fast_forward_infeasible(self):
''' Enter fast forward due to infeasible constraint. '''
network = self.simple_net
batch_size = 1
# Very small gbuf size. Small fmap tpart is infeasible.
resource = self.resource._replace(dim_array=PhyDim2(2, 2),
size_gbuf=16)
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True)
nnd = NNDataflow(network, batch_size, resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
# No pipelining is feasible.
for dtfl in tops:
self.assertTupleEqual(dtfl['1'].sched_seq, (0, 0, 0))
self.assertTupleEqual(dtfl['2'].sched_seq, (1, 0, 0))
def test_fast_forward_frontier(self):
''' Enter fast forward due to off-frontier. '''
network = self.simple_net
batch_size = 16
# Multiple nodes for spatial pipelining.
resource = self.resource._replace(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(8, 8),
type=NodeRegion.PROC),
dim_array=PhyDim2(2, 2),
)
# No time overhead limit.
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True,
layer_pipeline_time_ovhd=float('inf'))
nnd = NNDataflow(network, batch_size, resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
def test_fast_forward_crit_time(self):
''' Enter fast forward due to long critical time. '''
network = self.simple_net
batch_size = 1
# Multiple nodes for spatial pipelining.
resource = self.resource._replace(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(8, 8),
type=NodeRegion.PROC),
dim_array=PhyDim2(1, 1),
)
# Very strict time overhead limit.
# At large fmap tpart, utilization decreases and critical time would
# increase.
options = Option(hw_gbuf_save_writeback=True,
partition_interlayer=True,
layer_pipeline_time_ovhd=1e-3)
nnd = NNDataflow(network, batch_size, resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
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
def test_eyeriss_asplos17(self):
'''
Reproduce TETRIS ASPLOS'17 paper Figure 8.
'''
network = self.alex_net
batch_size = 16
## L-1 configuration.
resource = Resource(proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC),
dram_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(1, 1),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(1, 1),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(1, 1),
type=NodeRegion.DRAM),
dim_array=PhyDim2(16, 16),
size_gbuf=576056 // 2, # 576 kB
size_regf=1024 // 2, # 1 kB
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False,
)
cost = Cost(mac_op=2e-12,
mem_hier=(240e-12, 28e-12, 4e-12, 1e-12), # pJ/16-b
noc_hop=0,
idl_unit=320e-12)
nnd = NNDataflow(network, batch_size, resource, cost,
self.map_strategy)
tops, _ = nnd.schedule_search(self.options)
self.assertTrue(tops)
dfsch_l1 = tops[0]
## T-16 configuration.
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, 4),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dim_array=PhyDim2(14, 14),
size_gbuf=133032 // 2, # 133 kB
size_regf=512 // 2, # 512 B
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False,
)
cost = Cost(mac_op=2e-12,
mem_hier=(80e-12, 14e-12, 4e-12, 0.6e-12), # pJ/16-b
noc_hop=40e-12,
idl_unit=200e-12)
options = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
partition_hybrid=True)
nnd = NNDataflow(network, batch_size, resource, cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
dfsch_t16 = tops[0]
## Check results.
# Same workload.
self.assertAlmostEqual(dfsch_t16.total_ops, dfsch_l1.total_ops)
# Performance of T-16 is proportional to PE resource (20% margin).
self.assertLess(dfsch_t16.total_time,
1.2 * dfsch_l1.total_time * (16 * 16) / (14 * 14 * 16))
# Energy reduced by > 30%.
# self.assertLess(dfsch_t16.total_cost, dfsch_l1.total_cost * 0.7)
# With dimension restriction on partitioning, this is slightly violated.
self.assertLess(dfsch_t16.total_cost, dfsch_l1.total_cost * 0.72)
def test_eyeriss_isscc16(self):
'''
Reproduce Eyeriss ISSCC'16 paper Fig. 14.5.6, JSSC'17 paper Table V.
'''
network = self.alex_net
batch_size = 4
resource = Resource(proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC),
dram_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(1, 1),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(1, 1),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(1, 1),
type=NodeRegion.DRAM),
dim_array=PhyDim2(12, 14),
size_gbuf=108 * 1024 // 2, # 108 kB
size_regf=261, # 225 + 12 + 24
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False,
)
cost = Cost(mac_op=2e-12,
mem_hier=(460e-12, 15e-12, 4e-12, 1e-12), # pJ/16-b
noc_hop=0,
idl_unit=30e-3 / 200e6) # 30 mW GBUF + REGF
nnd = NNDataflow(network, batch_size, resource, cost,
self.map_strategy)
tops, _ = nnd.schedule_search(self.options)
self.assertTrue(tops)
dfsch = tops[0]
## Check results.
# Results as stats of the rows in the table.
header = 'Power, Processing Latency, Ops, Active PEs, Filter size'
stats = {}
for layer in ['conv{}'.format(i) for i in range(1, 6)]:
onchip_cost = 0
time = 0
ops = 0
fil_size = 0
for layer_part in network:
if not layer_part or not layer_part.startswith(layer):
continue
sr = dfsch[layer_part]
onchip_cost += sr.total_cost \
- sr.total_accesses[me.DRAM] * cost.mem_hier[me.DRAM]
time += sr.total_time
ops += sr.total_ops
fil_size += network[layer_part].total_filter_size()
power = onchip_cost / (time / 200e6) * 1e3 # mW
active_pes = int(ops / time)
stats[layer] = []
stats[layer].append(power)
stats[layer].append(time / 200.e3) # cycles to ms
stats[layer].append(ops / 1e6) # to MOPs
stats[layer].append(active_pes)
stats[layer].append(fil_size / 1e3) # to k
# Check.
stats_ref = {'conv1': [332, 16.5, 421.66, 151, 34.8], # Act PE 154
'conv2': [288, 39.2, 895.79, 135, 307.2],
'conv3': [266, 21.8, 598.1, 156, 884.7],
'conv4': [235, 16.0, 448.6, 156, 663.6],
'conv5': [236, 10.0, 299.0, 156, 442.4],
}
for layer in stats:
success = (0.6 * stats_ref[layer][0]
< stats[layer][0]
< stats_ref[layer][0]) \
and (0.8 * stats_ref[layer][1]
< stats[layer][1]
< stats_ref[layer][1]) \
and all(abs(a - b) < 0.1 for a, b
in zip(stats[layer][2:], stats_ref[layer][2:]))
self.assertTrue(success,
'test_eyeriss_isscc16: '
'stats diff in layer {}.\n'
'header: {}\n'
'actual: {}\nref: {}'
.format(layer, header, stats[layer],
stats_ref[layer]))
def test_eyeriss_isca16(self):
'''
Reproduce Eyeriss ISCA'16 paper Fig. 10.
'''
network = self.alex_net
batch_size = 16
nnd = NNDataflow(network, batch_size, self.resource, self.cost,
self.map_strategy)
tops, _ = nnd.schedule_search(self.options)
self.assertTrue(tops)
dfsch = tops[0]
## Check results.
# Results as cost for each component:
header = 'ALU, DRAM, Buffer, Array, RF'
cost_bkdn = {}
for layer in ['conv{}'.format(i) for i in range(1, 6)] \
+ ['fc{}'.format(i) for i in range(1, 4)]:
op_cost = 0
access_cost = [0] * me.NUM
for layer_part in network:
if not layer_part or not layer_part.startswith(layer):
continue
sr = dfsch[layer_part]
op_cost += sr.total_ops * self.cost.mac_op
access_cost = [ac + a * c for ac, a, c
in zip(access_cost, sr.total_accesses,
self.cost.mem_hier)]
cost_bkdn[layer] = []
# To 1e9.
cost_bkdn[layer].append(op_cost / 1e9)
cost_bkdn[layer].append(access_cost[me.DRAM] / 1e9)
cost_bkdn[layer].append(access_cost[me.GBUF] / 1e9)
cost_bkdn[layer].append(access_cost[me.ITCN] / 1e9)
cost_bkdn[layer].append(access_cost[me.REGF] / 1e9)
# Check the major parts: ALU, DRAM, RF.
major_cost_bkdn_ref = {'conv1': [1.69, 2.46, 6.75],
'conv2': [3.58, 2.27, 14.33],
'conv3': [2.39, 2.02, 9.57],
'conv4': [1.79, 1.57, 7.18],
'conv5': [1.20, 1.05, 4.78],
'fc1': [0.60, 7.78, 2.42],
'fc2': [0.27, 3.39, 1.07],
'fc3': [0.07, 0.84, 0.26],
}
for layer in cost_bkdn:
success = all(abs(a - b) < 0.1 for a, b
in zip(cost_bkdn[layer][:2] + cost_bkdn[layer][-1:],
major_cost_bkdn_ref[layer]))
self.assertTrue(success,
'test_eyeriss_isca16: '
'ALU, DRAM, RF cost diff in layer {}.\n'
'header: {}\n'
'actual: {}\nref: {}'
.format(layer, header, cost_bkdn[layer],
major_cost_bkdn_ref[layer]))
def eyerissAsplos17(self):
'''
Reproduce TETRIS ASPLOS'17 paper Figure 8.
'''
network = self.alex_net
batch_size = 16
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, 4),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dim_array=PhyDim2(14, 14),
size_gbuf=133032 // 2, # 133 kB
size_regf=512 // 2, # 512 B
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False,
)
cost = Cost(
mac_op=2e-12,
mem_hier=(80e-12, 14e-12, 4e-12, 0.6e-12), # pJ/16-b
noc_hop=40e-12,
idl_unit=200e-12)
options = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
partition_hybrid=True)
pdb.set_trace()
nnd = NNDataflow(network, batch_size, resource, cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
dfsch_t16 = tops[0]
## Check results.
# Same workload.
#self.assertAlmostEqual(dfsch_t16.total_ops, dfsch_l1.total_ops)
print('t16 ops: {}'.format(dfsch_t16.total_ops))
# Performance of T-16 is proportional to PE resource (20% margin).
#self.assertLess(dfsch_t16.total_time,
# 1.2 * dfsch_l1.total_time * (16 * 16) / (14 * 14 * 16))
print('t16_time: {}'.format(dfsch_t16.total_time))
# Energy reduced by > 30%.
# self.assertLess(dfsch_t16.total_cost, dfsch_l1.total_cost * 0.7)
# With dimension restriction on partitioning, this is slightly violated.
#self.assertLess(dfsch_t16.total_cost, dfsch_l1.total_cost * 0.72)
print('t16_energy: {}'.format(dfsch_t16.total_cost))
for i in dfsch_t16:
print(str(i) + ',')
## Check results.
# Results as cost for each component:
header = 'ALU, DRAM, Buffer, Array, RF'
cost_bkdn = {}
for layer in dfsch_t16:
layer = str(layer)
op_cost = 0
access_cost = [0] * me.NUM
for layer_part in network:
if not layer_part or not layer_part.startswith(layer):
continue
sr = dfsch_t16[layer_part]
op_cost += sr.total_ops * cost.mac_op
access_cost = [
ac + a * c for ac, a, c in zip(
access_cost, sr.total_accesses, cost.mem_hier)
]
cost_bkdn[layer] = []
# To 1e9.
cost_bkdn[layer].append(op_cost * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.DRAM] * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.GBUF] * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.ITCN] * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.REGF] * 1e12 / 1e9)
for layer in cost_bkdn:
print(cost_bkdn[layer])
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
def eyerissAsplos17(self):
'''
Reproduce TETRIS ASPLOS'17 paper Figure 8.
'''
#network = self.alex_net
network = self.mock_net
batch_size = 1
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, 4),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.DRAM),
dim_array=PhyDim2(14, 14),
size_gbuf=133032 // 2, # 133 kB
size_regf=512 // 2, # 512 B
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False,
num_value_pes=256,
)
# model values
print('converting weights')
q_weight_dict = {}
weights_dict = read_weights()
for w_layer in [
'conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7',
'fc8'
]:
array = convertToArray(weights_dict, w_layer)
array_qint8 = quantizeWeights(array, 'qint8')
q_weight_dict[w_layer] = array_qint8
#print('''Hey num weights in conv1 are {} '''.format(len(array_qint8)))
# hardware costs
mult_cost = readValueMult8Cost()
#control_cost = readValueControl8Cost()
print('done converting weights')
#with open('weights.pickle', 'wb') as f:
# pickle.dump(q_weight_dict,f)
#counter = 0
#c = 0
#for m in mult_cost.keys():
# c += mult_cost[m]
# counter += 1
#ave = c/counter
#print('{} '.format(counter))
#print('average = {}'.format(ave))
#print('conv3 weights are')
#for w in q_weight_dict['conv1']:
# print(w)
#exit()
cost = Cost(
value_control=1.92e-13,
value_mult=mult_cost,
mac_op=2e-12,
adder_cost=(1.178e-5) / 200000000,
mem_hier=(80e-12, 14e-12, 4e-12, 0.6e-12), # pj/16-b
noc_hop=40e-12,
idl_unit=200e-12,
my_weights=q_weight_dict,
mem_cycles=(200, 6, 2, 1))
#cost = cost(value_control=control_cost,
# value_mult=mult_cost,
# mac_op=2e-12,
# mem_hier=(80e-12, 14e-12, 4e-12, 0.6e-12), # pj/16-b
# noc_hop=40e-12,
# idl_unit=200e-12)
options = Option(sw_gbuf_bypass=(True, True, True),
sw_solve_loopblocking=True,
partition_hybrid=True)
#pdb.set_trace()
nnd = NNDataflow(network, batch_size, resource, cost,
self.map_strategy)
tops, _ = nnd.schedule_search(options)
self.assertTrue(tops)
dfsch_t16 = tops[0]
## Check results.
# Same workload.
#self.assertAlmostEqual(dfsch_t16.total_ops, dfsch_l1.total_ops)
print('t16 ops: {}'.format(dfsch_t16.total_ops))
# Performance of T-16 is proportional to PE resource (20% margin).
#self.assertLess(dfsch_t16.total_time,
# 1.2 * dfsch_l1.total_time * (16 * 16) / (14 * 14 * 16))
print('t16_time: {}'.format(dfsch_t16.total_time))
# Energy reduced by > 30%.
# self.assertLess(dfsch_t16.total_cost, dfsch_l1.total_cost * 0.7)
# With dimension restriction on partitioning, this is slightly violated.
#self.assertLess(dfsch_t16.total_cost, dfsch_l1.total_cost * 0.72)
print('t16_energy: {}'.format(dfsch_t16.total_cost))
for i in dfsch_t16:
print(str(i) + ',')
## Check results.
# Results as cost for each component:
header = 'ALU, DRAM, Buffer, Array, RF'
cost_bkdn = {}
for layer in dfsch_t16:
layer = str(layer)
op_cost = 0
access_cost = [0] * me.NUM
for layer_part in network:
if not layer_part or not layer_part.startswith(layer):
continue
sr = dfsch_t16[layer_part]
op_cost += sr.total_ops * cost.mac_op
access_cost = [
ac + a * c for ac, a, c in zip(
access_cost, sr.total_accesses, cost.mem_hier)
]
cost_bkdn[layer] = []
# To 1e9.
cost_bkdn[layer].append(op_cost * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.DRAM] * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.GBUF] * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.ITCN] * 1e12 / 1e9)
cost_bkdn[layer].append(access_cost[me.REGF] * 1e12 / 1e9)
for layer in cost_bkdn:
print(cost_bkdn[layer])
