triton/autotune/python/genetic.py

import random
import time
import sys
import tools
import pyviennacl as vcl
import numpy as np
import copy
from deap import algorithms

from collections import OrderedDict as odict

def closest_divisor(N, x):
  x_low=x_high=max(1,min(round(x),N))
  while N % x_low > 0 and x_low>0:
    x_low = x_low - 1
  while N % x_high > 0 and x_high < N:
    x_high = x_high + 1
  return x_low if x - x_low < x_high - x else x_high
    
class GeneticOperators(object):
  
  def __init__(self, device, statement, parameters, parameter_names, TemplateType, build_template):
      self.device = device
      self.statement = statement
      self.parameters = parameters
      self.parameter_names = parameter_names
      self.TemplateType = TemplateType
      self.ParameterType = TemplateType.Parameters
      self.build_template = build_template
      self.cache = {}
      self.indpb = 0.15
    
  def init(self):
      while True:
        result = [random.choice(L) for L in self.parameters]
        template = self.build_template(self.TemplateType.Parameters(*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 template.check(self.statement)==0 and occupancy_record.occupancy >= 10 :
          return result

  def mutate(self, individual):
    while True:
      new_individual = copy.deepcopy(individual)
      for i in new_individual:
        if random.random() < self.indpb:
          coef = random.choice([1, 2])
          funs = [lambda x:max(1, x/coef), lambda x:x*coef]
          F = random.choice(funs)
          nF = funs[1] if F==funs[0] else funs[0]
          #swapping-based mutations
          def m0():
            new_individual[1], new_individual[3] = new_individual[3], new_individual[1]
          def m1():
            new_individual[4], new_individual[6] = new_individual[6], new_individual[4]
          def m2():
            new_individual[9], new_individual[10] = new_individual[10], new_individual[9]
          #value modification mutations
          def m3():
            new_individual[0] = random.choice(self.parameters[0])
          def m4():
            new_individual[1] = F(new_individual[1])
            new_individual[9] = F(new_individual[9])
          def m5():
            new_individual[2] = F(new_individual[2])
          def m6():
            new_individual[3] = F(new_individual[3])
            new_individual[10] = F(new_individual[10])
          def m7():
            new_individual[4] = F(new_individual[4])
          def m8():
            new_individual[5] = F(new_individual[5])
          def m9():
            new_individual[6] = F(new_individual[6])
          def m10():
            new_individual[7] = random.choice([x for x in self.parameters[7] if x!=new_individual[7]])
          def m11():
            new_individual[8] = random.choice([x for x in self.parameters[8] if x!=new_individual[8]])
          def m12():
            new_individual[9] = F(new_individual[9])
            new_individual[10] = nF(new_individual[10])
          def m13():
            new_individual[10] = F(new_individual[10])
            new_individual[9] = nF(new_individual[9])
          random.choice([m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13])()
      template = self.build_template(self.TemplateType.Parameters(*new_individual))
      if not tools.skip(template, self.statement, self.device):
        break
    return new_individual,
      
  def evaluate(self, individual):
    if tuple(individual) not in self.cache:
      template = self.build_template(self.TemplateType.Parameters(*individual))
      try:
        self.cache[tuple(individual)] = tools.benchmark(template, self.statement, self.device)
      except:
        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):
        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 ]\r'%(gen, time.time() - start_time, best_performance, perf_metric, best_profile))
        sys.stdout.flush()
    sys.stdout.write('\n')
    return population