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Added exhaustive search backend

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Philippe Tillet
2014-09-11 16:13:46 -04:00
parent bf48d69b17
commit 08d17aa58c
4 changed files with 119 additions and 77 deletions
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78
autotune/python/optimize.py
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@@ -1,57 +1,39 @@
import array
import numpy as np
import random
import time
import sys
from deap import algorithms
import itertools
import tools
import deap.tools
from deap import base
from deap import creator
from deap import tools
from genetic import GeneticOperators
from genetic import eaMuPlusLambda
from genetic_operators import GeneticOperators
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):
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)
# 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)
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)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
gen = gen + 1
best_profile = '(%s)'%','.join(map(str,halloffame[0]));
best_performance = compute_perf(halloffame[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('\n')
return population
def exhaustive(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
device = context.devices[0]
nvalid = 0
current = 0
minT = float('inf')
for individual in itertools.product(*all_parameters):
template = build_template(TemplateType.Parameters(*individual))
if not tools.skip(template, statement, device):
nvalid = nvalid + 1
for individual in itertools.product(*all_parameters):
template = build_template(TemplateType.Parameters(*individual))
try:
T = tools.benchmark(template,statement,device)
current = current + 1
if T < minT:
minT = T
best = individual
print '%d / %d , Best is %d %s for %s\r'%(current, nvalid, compute_perf(minT), perf_metric, best)
except:
pass
def genetic(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
gen = GeneticOperators(context.devices[0], statement, all_parameters, parameter_names, TemplateType, build_template)
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
@@ -68,12 +50,12 @@ def genetic(statement, context, TemplateType, build_template, parameter_names, a
toolbox.register("select", tools.selBest)
pop = toolbox.population(n=30)
hof = tools.HallOfFame(1)
hof = deap.tools.HallOfFame(1)
best_performer = lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x])
best_profile = lambda x: '(%s)'%','.join(map(str,hof[0]))
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats = deap.tools.Statistics(lambda ind: ind.fitness.values)
stats.register("max (" + perf_metric + ")", lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x]))
stats.register("profile ", lambda x: '(%s)'%','.join(map(str,hof[0])))