Porting GA for all the operations
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@@ -33,10 +33,9 @@ def b_gray_to_bin(A='00000000', endian='big'):
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class GeneticOperators(object):
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def __init__(self, device, statement, parameter_names, TemplateType, build_template, out):
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def __init__(self, device, statement, TemplateType, build_template, out):
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self.device = device
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self.statement = statement
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self.parameter_names = parameter_names
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self.TemplateType = TemplateType
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self.ParameterType = TemplateType.Parameters
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self.build_template = build_template
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@@ -44,6 +43,11 @@ class GeneticOperators(object):
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self.indpb = 0.05
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self.out = out
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self.genome_info = {
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vcl.atidlas.VectorAxpyTemplate: [3,4,4,vcl.atidlas.FetchingPolicy],
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vcl.atidlas.MatrixProductTemplate: [3,3,3,3,3,3,3,vcl.atidlas.FetchingPolicy,vcl.atidlas.FetchingPolicy,3]
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}[TemplateType]
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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@@ -54,35 +58,39 @@ class GeneticOperators(object):
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self.toolbox.register("mutate", self.mutate)
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self.toolbox.register("select", deap_tools.selNSGA2)
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@staticmethod
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def decode(s):
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def decode(self, genome):
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FetchingPolicy = vcl.atidlas.FetchingPolicy
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fetch = [FetchingPolicy.FETCH_FROM_LOCAL, FetchingPolicy.FETCH_FROM_GLOBAL_CONTIGUOUS, FetchingPolicy.FETCH_FROM_GLOBAL_STRIDED]
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fetchA = fetch[s[0]]
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fetchB = fetch[s[1]]
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bincode = ''.join(s[2:])
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decode_element = lambda x:2**int(b_gray_to_bin(x), 2)
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simd = decode_element(bincode[0:3])
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ls0 = decode_element(bincode[2:5])
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ls1 = decode_element(bincode[5:8])
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kL = decode_element(bincode[8:11])
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mS = decode_element(bincode[11:14])
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kS = decode_element(bincode[14:17])
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nS = decode_element(bincode[17:20])
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if fetchA==FetchingPolicy.FETCH_FROM_LOCAL or fetchB==FetchingPolicy.FETCH_FROM_LOCAL:
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lf0 = decode_element(bincode[20:23])
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lf1 = ls0*ls1/lf0
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else:
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lf0, lf1 = 0, 0
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return [simd, ls0, kL, ls1, mS, kS, nS, fetchA, fetchB, lf0, lf1]
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decode_element = lambda x:2**int(b_gray_to_bin(''.join(x)), 2)
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result = []
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offset = 0
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for x in self.genome_info:
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if x==vcl.atidlas.FetchingPolicy:
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result.append(fetch[genome[offset]])
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offset = offset + 1
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else:
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result.append(decode_element(genome[offset:offset+x]))
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offset = offset + x
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#GEMM peculiarities
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if self.TemplateType==vcl.atidlas.MatrixProductTemplate:
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if FetchingPolicy.FETCH_FROM_LOCAL in result:
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lf1 = result[1]*result[3]/result[9]
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else:
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result[9] = 0
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lf1 = 0
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result.append(lf1)
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return result
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def init(self, N):
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result = []
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fetchcount = [0, 0, 0]
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while len(result) < N:
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while True:
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fetch = random.randint(0,2)
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bincode = [fetch, fetch] + [str(random.randint(0,1)) for i in range(23)]
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bincode = []
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for x in self.genome_info:
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if x==vcl.atidlas.FetchingPolicy:
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bincode = bincode + [random.randint(0,2)]
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else:
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bincode = bincode + [str(random.randint(0,1)) for i in range(x)]
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parameters = self.decode(bincode)
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template = self.build_template(self.TemplateType.Parameters(*parameters))
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registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4
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@@ -90,22 +98,18 @@ class GeneticOperators(object):
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local_size = template.parameters.local_size_0*template.parameters.local_size_1
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occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage)
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if not tools.skip(template, self.statement, self.device):
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fetchcount[fetch] = fetchcount[fetch] + 1
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if max(fetchcount) - min(fetchcount) <= 1:
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result.append(creator.Individual(bincode))
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break
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else:
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fetchcount[fetch] = fetchcount[fetch] - 1
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result.append(creator.Individual(bincode))
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break
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return result
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def mutate(self, individual):
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while True:
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new_individual = copy.deepcopy(individual)
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for i in range(len(new_individual)):
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if i < 2 and random.random() < self.indpb:
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if isinstance(individual[i], int) and random.random() < self.indpb:
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while new_individual[i] == individual[i]:
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new_individual[i] = random.randint(0, 2)
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elif i >= 2 and random.random() < self.indpb:
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elif not isinstance(individual[i], int) and random.random() < self.indpb:
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new_individual[i] = '1' if new_individual[i]=='0' else '0'
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parameters = self.decode(new_individual)
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template = self.build_template(self.TemplateType.Parameters(*parameters))
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@@ -176,7 +180,7 @@ class GeneticOperators(object):
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population[:] = self.toolbox.select(population + offspring, mu)
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#Update
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gen = gen + 1
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best_profile = '(%s)'%','.join(map(str,GeneticOperators.decode(hof[0])));
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best_profile = '(%s)'%','.join(map(str,self.decode(hof[0])));
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best_performance = compute_perf(hof[0].fitness.values[0])
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sys.stdout.write('Time %d | Best %d %s [ for %s ]\r'%(time.time() - start_time, best_performance, perf_metric, best_profile))
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sys.stdout.flush()
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