gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

More efficient parameters

Browse Source
This commit is contained in:
Philippe Tillet
2014-09-15 18:30:30 -04:00
parent 64344f3a0a
commit 878cefa29b
2 changed files with 89 additions and 74 deletions
Download Patch File Download Diff File Expand all files Collapse all files

137
autotune/python/genetic.py
View File

@@ -5,7 +5,11 @@ import tools
import pyviennacl as vcl
import numpy as np
import copy
from deap import algorithms
from deap import base
from deap import creator
from deap import tools as deap_tools
from collections import OrderedDict as odict
@@ -39,7 +43,17 @@ class GeneticOperators(object):
self.build_template = build_template
self.cache = {}
self.indpb = 0.1
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
self.toolbox = base.Toolbox()
self.toolbox.register("population", self.init)
self.toolbox.register("evaluate", self.evaluate)
self.toolbox.register("mate", deap_tools.cxTwoPoint)
self.toolbox.register("mutate", self.mutate)
self.toolbox.register("select", deap_tools.selBest)
@staticmethod
def decode(s):
FetchingPolicy = vcl.atidlas.FetchingPolicy
@@ -62,30 +76,41 @@ class GeneticOperators(object):
lf0, lf1 = 0, 0
return [simd, ls0, kL, ls1, mS, kS, nS, fetchA, fetchB, lf0, lf1]
def init(self):
while True:
result = [random.randint(0,2), random.randint(0,2)] + [str(random.randint(0,1)) for i in range(23)]
template = self.build_template(self.TemplateType.Parameters(*self.decode(result)))
registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4
lmem_usage = template.lmem_usage(vcl.atidlas.StatementsTuple(self.statement))
local_size = template.parameters.local_size_0*template.parameters.local_size_1
occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage)
if not tools.skip(template, self.statement, self.device):
return result
def init(self, N):
result = []
def generate(Afetch, Bfetch, K):
result = []
while len(result) < K:
bincode = [Afetch, Bfetch] + [str(random.randint(0,1)) for i in range(23)]
parameters = self.decode(bincode)
template = self.build_template(self.TemplateType.Parameters(*parameters))
registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4
lmem_usage = template.lmem_usage(vcl.atidlas.StatementsTuple(self.statement))
local_size = template.parameters.local_size_0*template.parameters.local_size_1
occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage)
if not tools.skip(template, self.statement, self.device):
result.append(creator.Individual(bincode))
return result
result += generate(0,0,N/3)
result += generate(1,1,N/3)
result += generate(2,2,N/3)
return result
def mutate(self, individual):
while True:
new_individual = copy.deepcopy(individual)
for i in range(len(new_individual)):
if random.random() < self.indpb:
if i < 2:
while new_individual[i] == individual[i]:
new_individual[i] = random.randint(0, 2)
else:
new_individual[i] = '1' if new_individual[i]=='0' else '0'
if i < 2 and random.random() < self.indpb:
while new_individual[i] == individual[i]:
new_individual[i] = random.randint(0, 2)
elif i >= 2 and random.random() < self.indpb:
new_individual[i] = '1' if new_individual[i]=='0' else '0'
parameters = self.decode(new_individual)
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):
break
return new_individual,
@@ -100,44 +125,58 @@ class GeneticOperators(object):
self.cache[tuple(individual)] = 10
return self.cache[tuple(individual)],
def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, maxtime, maxgen, halloffame, compute_perf, perf_metric):
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
def optimize(self, maxtime, maxgen, compute_perf, perf_metric):
hof = deap_tools.HallOfFame(1)
# Begin the generational process
gen = 0
maxtime = time.strptime(maxtime, '%Mm%Ss')
maxtime = maxtime.tm_min*60 + maxtime.tm_sec
start_time = time.time()
mu = 30
_lambda = 50
cxpb = 0.4
mutpb = 0.5
population = self.init(mu)
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
# Begin the generational process
gen = 0
maxtime = time.strptime(maxtime, '%Mm%Ss')
maxtime = maxtime.tm_min*60 + maxtime.tm_sec
start_time = time.time()
while time.time() - start_time < maxtime and gen < maxgen:
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
hof.update(population)
while time.time() - start_time < maxtime:
# Vary the population
offspring = []
for _ in xrange(_lambda):
op_choice = random.random()
if op_choice < cxpb: # Apply crossover
ind1, ind2 = map(self.toolbox.clone, random.sample(population, 2))
ind1, ind2 = self.toolbox.mate(ind1, ind2)
del ind1.fitness.values
offspring.append(ind1)
elif op_choice < cxpb + mutpb: # Apply mutation
ind = self.toolbox.clone(random.choice(population))
ind, = self.toolbox.mutate(ind)
del ind.fitness.values
offspring.append(ind)
else: # Apply reproduction
offspring.append(random.choice(population))
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
fitnesses = self.toolbox.map(self.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
hof.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
population[:] = self.toolbox.select(population + offspring, mu)
gen = gen + 1
best_profile = '(%s)'%','.join(map(str,GeneticOperators.decode(halloffame[0])));
best_performance = compute_perf(halloffame[0].fitness.values[0])
best_profile = '(%s)'%','.join(map(str,GeneticOperators.decode(hof[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.flush()
sys.stdout.write('\n')
return population
sys.stdout.write('\n')
return population