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Better initialization

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This commit is contained in:
Philippe Tillet
2014-09-21 16:07:19 -04:00
parent 878cefa29b
commit 76dbb9a42f
1 changed files with 20 additions and 22 deletions
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40
autotune/python/genetic.py
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@@ -27,7 +27,6 @@ def b_gray_to_bin(A='00000000', endian='big'):
if endian == 'little': A = A[::-1] # Make sure endianness is big before conversion if endian == 'little': A = A[::-1] # Make sure endianness is big before conversion
b = A[0] b = A[0]
for i in range(1, len(A)): b += str( int(b[i-1] != A[i]) ) for i in range(1, len(A)): b += str( int(b[i-1] != A[i]) )
assert len(A) == len(b), 'Error in this function! len(A) must equal len(b). Oh dear.'
if endian == 'little': b = b[::-1] # Convert back to little endian if necessary if endian == 'little': b = b[::-1] # Convert back to little endian if necessary
return b return b
@@ -42,7 +41,7 @@ class GeneticOperators(object):
self.ParameterType = TemplateType.Parameters self.ParameterType = TemplateType.Parameters
self.build_template = build_template self.build_template = build_template
self.cache = {} self.cache = {}
self.indpb = 0.1 self.indpb = 0.05
creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin) creator.create("Individual", list, fitness=creator.FitnessMin)
@@ -52,7 +51,7 @@ class GeneticOperators(object):
self.toolbox.register("evaluate", self.evaluate) self.toolbox.register("evaluate", self.evaluate)
self.toolbox.register("mate", deap_tools.cxTwoPoint) self.toolbox.register("mate", deap_tools.cxTwoPoint)
self.toolbox.register("mutate", self.mutate) self.toolbox.register("mutate", self.mutate)
self.toolbox.register("select", deap_tools.selBest) self.toolbox.register("select", deap_tools.selNSGA2)
@staticmethod @staticmethod
def decode(s): def decode(s):
@@ -78,12 +77,11 @@ class GeneticOperators(object):
def init(self, N): def init(self, N):
result = [] result = []
fetchcount = [0, 0, 0]
while len(result) < N:
def generate(Afetch, Bfetch, K): while True:
result = [] fetch = random.randint(0,2)
while len(result) < K: bincode = [fetch, fetch] + [str(random.randint(0,1)) for i in range(23)]
bincode = [Afetch, Bfetch] + [str(random.randint(0,1)) for i in range(23)]
parameters = self.decode(bincode) parameters = self.decode(bincode)
template = self.build_template(self.TemplateType.Parameters(*parameters)) template = self.build_template(self.TemplateType.Parameters(*parameters))
registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4 registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4
@@ -91,26 +89,26 @@ class GeneticOperators(object):
local_size = template.parameters.local_size_0*template.parameters.local_size_1 local_size = template.parameters.local_size_0*template.parameters.local_size_1
occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage) occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage)
if not tools.skip(template, self.statement, self.device): if not tools.skip(template, self.statement, self.device):
fetchcount[fetch] = fetchcount[fetch] + 1
if max(fetchcount) - min(fetchcount) <= 1:
result.append(creator.Individual(bincode)) result.append(creator.Individual(bincode))
return result break
else:
result += generate(0,0,N/3) fetchcount[fetch] = fetchcount[fetch] - 1
result += generate(1,1,N/3)
result += generate(2,2,N/3)
return result return result
def mutate(self, individual): def mutate(self, individual):
while True: while True:
new_individual = copy.deepcopy(individual) new_individual = copy.deepcopy(individual)
for i in range(len(new_individual)): for i in range(len(new_individual)):
if i < 2 and random.random() < self.indpb: if i < 2 and random.random() < 0.1:
while new_individual[i] == individual[i]: while new_individual[i] == individual[i]:
new_individual[i] = random.randint(0, 2) new_individual[i] = random.randint(0, 2)
elif i >= 2 and random.random() < self.indpb: elif i >= 2 and random.random() < self.indpb:
new_individual[i] = '1' if new_individual[i]=='0' else '0' new_individual[i] = '1' if new_individual[i]=='0' else '0'
parameters = self.decode(new_individual) parameters = self.decode(new_individual)
template = self.build_template(self.TemplateType.Parameters(*parameters)) template = self.build_template(self.TemplateType.Parameters(*parameters))
#print tools.skip(template, self.statement, self.device), parameters
if not tools.skip(template, self.statement, self.device): if not tools.skip(template, self.statement, self.device):
break break
return new_individual, return new_individual,
@@ -133,10 +131,10 @@ class GeneticOperators(object):
maxtime = maxtime.tm_min*60 + maxtime.tm_sec maxtime = maxtime.tm_min*60 + maxtime.tm_sec
start_time = time.time() start_time = time.time()
mu = 30 mu = 70
_lambda = 50 _lambda = 100
cxpb = 0.4 cxpb = 0.2
mutpb = 0.5 mutpb = 0.7
population = self.init(mu) population = self.init(mu)
invalid_ind = [ind for ind in population if not ind.fitness.valid] invalid_ind = [ind for ind in population if not ind.fitness.valid]
@@ -174,7 +172,7 @@ class GeneticOperators(object):
gen = gen + 1 gen = gen + 1
best_profile = '(%s)'%','.join(map(str,GeneticOperators.decode(hof[0]))); best_profile = '(%s)'%','.join(map(str,GeneticOperators.decode(hof[0])));
best_performance = compute_perf(hof[0].fitness.values[0]) best_performance = compute_perf(hof[0].fitness.values[0])
sys.stdout.write('Generation %d | Time %d | Best %d %s [ for %s ]\n'%(gen, time.time() - start_time, best_performance, perf_metric, best_profile)) sys.stdout.write('Time %d | Best %d %s [ for %s ]\r'%(time.time() - start_time, best_performance, perf_metric, best_profile))
sys.stdout.flush() sys.stdout.flush()
sys.stdout.write('\n') sys.stdout.write('\n')
return population return population