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Trying a new mutation operator

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This commit is contained in:
Philippe Tillet
2014-09-10 11:10:19 -04:00
parent 46567ff812
commit 539efa029f
4 changed files with 57 additions and 21 deletions
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57
autotune/python/genetic_operators.py
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@@ -53,10 +53,13 @@ class GeneticOperators(object):
return (new_x, new_y)
def repair(self,func):
def repair_impl(child):
D = odict(zip(self.parameter_names, child))
dummy_template = self.build_template(self.ParameterType(*D.values()))
FetchingPolicy = vcl.atidlas.FetchingPolicy;
D['local-size-0'] = max(1, D['local-size-0'])
D['local-size-1'] = max(1, D['local-size-1'])
if 'local-size-1' not in D:
D['local-size-0'] = min(D['local-size-0'], self.device.max_work_group_size)
elif D['local-size-0']*D['local-size-1'] > self.device.max_work_group_size:
@@ -67,11 +70,13 @@ class GeneticOperators(object):
if self.ParameterType is vcl.atidlas.MatrixProductTemplate.Parameters:
if dummy_template.A_trans != 'N' and dummy_template.B_trans != 'T':
D['simd-width'] = 1
D['mS'] = max(D['mS'], D['simd-width'])
D['mS'] = D['mS'] - D['mS']%D['simd-width']
D['kL'] = max(1, D['kL'])
D['kS'] = max(1, D['kS'])
D['mS'] = max(D['mS'], D['simd-width'])
D['nS'] = max(D['nS'], D['simd-width'])
D['mS'] = D['mS'] - D['mS']%D['simd-width']
D['nS'] = D['nS'] - D['nS']%D['simd-width']
@@ -100,16 +105,48 @@ class GeneticOperators(object):
for i in range(len(child)):
if child[i] != new_child[i]:
child[i] = new_child[i]
return offspring
return wrappper
def mutate(self, individual, indpb):
for i in range(len(individual)):
if random.random() < indpb:
j = self.parameters[i].index(individual[i])
j = max(0,min(random.randint(j-2, j+2),len(self.parameters[i])-1))
individual[i] = self.parameters[i][j]
def mutate(self, individual):
p = random.random()
coef = random.choice([2, 4])
multiply_or_divide = random.choice([lambda x:x/coef, lambda x:x*coef])
if p < 0.1 :
individual[0] = random.choice(self.parameters[0])
elif p < 0.2:
idx = random.choice([1, 4])
nidx = 4 if idx==1 else 1
individual[idx] = individual[idx]*coef
individual[nidx] = individual[nidx]/coef
elif p < 0.3:
idx = random.choice([1, 4])
individual[idx] = multiply_or_divide(individual[idx])
if idx==1:
individual[9] = multiply_or_divide(individual[9])
elif p < 0.4:
idx = random.choice([3, 6])
nidx = 6 if idx==3 else 3
individual[idx] = individual[idx]*coef
individual[nidx] = individual[nidx]/coef
elif p < 0.5:
idx = random.choice([3, 6])
individual[idx] = multiply_or_divide(individual[idx])
if idx==3:
individual[10] = multiply_or_divide(individual[10])
elif p < 0.6:
individual[2] = multiply_or_divide(individual[2])
elif p < 0.7:
individual[4] = multiply_or_divide(individual[4])
elif p < 0.8:
individual[7] = random.choice([x for x in self.parameters[7] if x!=individual[7]])
elif p < 0.9:
individual[8] = random.choice([x for x in self.parameters[8] if x!=individual[8]])
elif p < 1:
idx = random.choice([9, 10])
nidx = 10 if idx==9 else 9
individual[idx] = individual[idx]*coef
individual[nidx] = individual[nidx]/coef
return individual,
def evaluate(self, individual):