Auto-tuner : Initial push

autotune/optimize.py

@@ -0,0 +1,92 @@
+import array
+import numpy as np
+import random
+import time
+
+from deap import algorithms
+from deap import base
+from deap import creator
+from deap import tools
+
+from genetic_operators import GeneticOperators
+
+def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, maxtime,
+                   stats=None, halloffame=None, verbose=__debug__):
+    logbook = tools.Logbook()
+    logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
+
+    # 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)
+
+    record = stats.compile(population) if stats is not None else {}
+    logbook.record(gen=0, nevals=len(invalid_ind), **record)
+    if verbose:
+        print logbook.stream
+
+    # 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:
+        # 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
+        record = stats.compile(population) if stats is not None else {}
+        logbook.record(gen=gen, nevals=len(invalid_ind), **record)
+        if verbose:
+            print logbook.stream
+
+    return population, logbook
+    
+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,))
+  creator.create("Individual", list, fitness=creator.FitnessMin)
+
+  toolbox = base.Toolbox()
+  toolbox.register("individual", tools.initIterate, creator.Individual, gen.init)
+  toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+  toolbox.decorate("population", gen.repair)
+  toolbox.register("evaluate", gen.evaluate)
+  toolbox.register("mate", tools.cxUniform, indpb=0.3)
+  toolbox.decorate("mate", gen.repair)
+  toolbox.register("mutate", gen.mutate, indpb=0.2)
+  toolbox.decorate("mutate", gen.repair)
+  toolbox.register("select", tools.selNSGA2)
+    
+  pop = toolbox.population(n=10)
+  hof = 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]))
+
+  cxpb = 0.5
+  mutpb = 0.2
+  
+  stats = 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])))
+
+  pop, log = eaMuPlusLambda(pop, toolbox, 10, 20, cxpb=0.2, mutpb=0.2, maxtime='5m0s', stats=stats, halloffame=hof, verbose=True)