40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import array
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import numpy as np
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import random
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import sys
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import itertools
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import tools
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import deap.tools
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from genetic import GeneticOperators
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def exhaustive(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
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device = context.devices[0]
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nvalid = 0
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current = 0
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minT = float('inf')
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for individual in itertools.product(*all_parameters):
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template = build_template(TemplateType.Parameters(*individual))
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if not tools.skip(template, statement, device):
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nvalid = nvalid + 1
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for individual in itertools.product(*all_parameters):
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template = build_template(TemplateType.Parameters(*individual))
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try:
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T = tools.benchmark(template,statement,device)
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current = current + 1
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if T < minT:
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minT = T
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best = individual
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sys.stdout.write('%d / %d , Best is %d %s for %s\r'%(current, nvalid, compute_perf(minT), perf_metric, best))
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sys.stdout.flush()
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except:
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pass
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sys.stdout.write('\n')
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sys.stdout.flush()
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def genetic(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
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GA = GeneticOperators(context.devices[0], statement, all_parameters, parameter_names, TemplateType, build_template)
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GA.optimize(maxtime='5m0s', maxgen=1000, compute_perf=compute_perf, perf_metric=perf_metric)
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