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No longer repair the GA ; kill the invalid mutants instead

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This commit is contained in:
Philippe Tillet
2014-09-13 17:06:47 -04:00
parent 5ee9e7f994
commit c4c8404d40
4 changed files with 64 additions and 132 deletions
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8
autotune/python/autotune.py
View File

@@ -48,10 +48,10 @@ TYPES = { 'vector-axpy': {'template':vcl.atidlas.VectorAxpyTemplate,
def parameter_space(operation):
simd = [1, 2, 4, 8]
pow2_1D = [2**k for k in range(12)]
pow2_2D = [8, 16]
pow2_2D_unrolled = [1, 2, 4, 8]
pow2_2D = [2**i for i in range(8)]
pow2_2D_unrolled = [2**i for i in range(8)]
FetchingPolicy = vcl.atidlas.FetchingPolicy
fetch = [FetchingPolicy.FETCH_FROM_LOCAL]
fetch = [FetchingPolicy.FETCH_FROM_LOCAL, FetchingPolicy.FETCH_FROM_GLOBAL_CONTIGUOUS, FetchingPolicy.FETCH_FROM_GLOBAL_STRIDED]
if operation == 'vector-axpy': return [simd, pow2_1D, pow2_1D, fetch]
if operation == 'reduction': return [simd, pow2_1D, pow2_1D, fetch]
if operation == 'matrix-axpy': return [simd, pow2_2D, pow2_2D, pow2_2D, pow2_2D, fetch]
@@ -97,7 +97,7 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
fname = os.devnull
with open(fname, "w+") as archive:
with vcl.Statement(node) as statement:
result = optimize.exhaustive(statement, ctx, TYPES[operation]['template'], lambda p: TYPES[operation]['template'](p, *other_params),
result = optimize.genetic(statement, ctx, TYPES[operation]['template'], lambda p: TYPES[operation]['template'](p, *other_params),
TYPES[operation]['parameter-names'], parameter_space(operation), lambda t: TYPES[operation]['perf-index']([datatype().itemsize, s, t]), TYPES[operation]['perf-measure'], archive)
if result and viennacl_root:
vclio.generate_viennacl_headers(viennacl_root, device, datatype, operation, other_params, result[1])

166
autotune/python/genetic.py
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@@ -3,8 +3,8 @@ import time
import sys
import tools
import pyviennacl as vcl
import numpy
import numpy as np
import copy
from deap import algorithms
from collections import OrderedDict as odict
@@ -28,6 +28,7 @@ class GeneticOperators(object):
self.ParameterType = TemplateType.Parameters
self.build_template = build_template
self.cache = {}
self.indpb = 0.15
def init(self):
while True:
@@ -40,121 +41,54 @@ class GeneticOperators(object):
if template.check(self.statement)==0 and occupancy_record.occupancy >= 10 :
return result
@staticmethod
def min_to_hyperbol(a, tup):
x = 1
for i in range(100):
dx = 2*(-a**2/x**3 + a*tup[1]/x**2 - tup[0] + x);
ddx = 6*a**2/x**4 - 4*a*tup[1]/x**3 + 2;
if abs(dx) < 1e-7 or abs(ddx) < 1e-7:
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
x-=dx/ddx;
if x<1 or x>a:
x = max(1, min(x, a))
break
new_x = int(closest_divisor(a, x))
new_y = int(a / new_x)
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:
res = GeneticOperators.min_to_hyperbol(self.device.max_work_group_size, (D['local-size-0'], D['local-size-1']))
D['local-size-0'] = res[0]
D['local-size-1'] = res[1]
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['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']
if D['A-fetch-policy']!=FetchingPolicy.FETCH_FROM_LOCAL and D['B-fetch-policy']!=FetchingPolicy.FETCH_FROM_LOCAL:
D['local-fetch-size-0']=D['local-fetch-size-1']=0
else:
res = GeneticOperators.min_to_hyperbol(D['local-size-0']*D['local-size-1'], (D['local-fetch-size-0'], D['local-fetch-size-1']))
D['local-fetch-size-0'] = res[0]
D['local-fetch-size-1'] = res[1]
if D['A-fetch-policy']==FetchingPolicy.FETCH_FROM_LOCAL and dummy_template.A_trans=='N' and D['kL'] % D['local-fetch-size-1'] > 0:
D['kL'] = max(1,round(D['kL']/D['local-fetch-size-1']))*D['local-fetch-size-1']
if D['B-fetch-policy']==FetchingPolicy.FETCH_FROM_LOCAL and dummy_template.B_trans=='T' and D['kL'] % D['local-fetch-size-1'] > 0:
D['kL'] = max(1,round(D['kL']/D['local-fetch-size-1']))*D['local-fetch-size-1']
D['kS'] = min(D['kL'], D['kS'])
return D.values()
def wrappper(*args, **kargs):
offspring = func(*args, **kargs)
for child in offspring:
new_child = repair_impl(child)
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 = 0.15):
for i in individual:
if random.random() < indpb:
coef = 2**(1 + numpy.random.poisson())
funs = [lambda x: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():
individual[1], individual[3] = individual[3], individual[1]
def m1():
individual[4], individual[6] = individual[6], individual[4]
def m2():
individual[9], individual[10] = individual[10], individual[9]
#value modification mutations
def m3():
individual[0] = random.choice(self.parameters[0])
def m4():
individual[1] = F(individual[1])
individual[9] = F(individual[9])
def m5():
individual[2] = F(individual[2])
def m6():
individual[3] = F(individual[3])
individual[10] = F(individual[10])
def m7():
individual[4] = F(individual[4])
def m8():
individual[5] = F(individual[5])
def m9():
individual[6] = F(individual[6])
def m10():
individual[7] = random.choice([x for x in self.parameters[7] if x!=individual[7]])
def m11():
individual[8] = random.choice([x for x in self.parameters[8] if x!=individual[8]])
def m12():
individual[9] = F(individual[9])
individual[10] = nF(individual[10])
def m13():
individual[10] = F(individual[10])
individual[9] = nF(individual[9])
random.choice([m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13])()
return individual,
return new_individual,
def evaluate(self, individual):
if tuple(individual) not in self.cache:

20
autotune/python/optimize.py
View File

@@ -38,21 +38,19 @@ def exhaustive(statement, context, TemplateType, build_template, parameter_names
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)
GA = 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.register("evaluate", gen.evaluate)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.decorate("mate", gen.repair)
toolbox.register("mutate", gen.mutate)
toolbox.decorate("mutate", gen.repair)
toolbox.register("select", tools.selBest)
toolbox.register("individual", deap.tools.initIterate, creator.Individual, GA.init)
toolbox.register("population", deap.tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", GA.evaluate)
toolbox.register("mate", deap.tools.cxTwoPoint)
toolbox.register("mutate", GA.mutate)
toolbox.register("select", deap.tools.selBest)
pop = toolbox.population(n=30)
pop = toolbox.population(n=50)
hof = deap.tools.HallOfFame(1)
best_performer = lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x])
@@ -62,4 +60,4 @@ def genetic(statement, context, TemplateType, build_template, parameter_names, a
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 = eaMuPlusLambda(pop, toolbox, 30, 50, cxpb=0.2, mutpb=0.3, maxtime='3m0s', maxgen=200, halloffame=hof, compute_perf=compute_perf, perf_metric=perf_metric)
pop = eaMuPlusLambda(pop, toolbox, 50, 70, cxpb=0.2, mutpb=0.3, maxtime='5m0s', maxgen=500, halloffame=hof, compute_perf=compute_perf, perf_metric=perf_metric)

2
autotune/python/tools.py
View File

@@ -79,7 +79,7 @@ class OccupancyRecord:
def __init__(self, dev, threads, shared_mem=0, registers=0):
physical_limits = PhysicalLimits(dev)
limits = [];
allocated_warps = _int_ceiling(threads/physical_limits.threads_per_warp)
allocated_warps = max(1,_int_ceiling(threads/physical_limits.threads_per_warp))
max_warps_per_mp = physical_limits.warps_per_mp;
limits.append((min(physical_limits.thread_blocks_per_mp, _int_floor(max_warps_per_mp/allocated_warps)), 'warps'))