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Squashed feature branch:

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* Added CUDA support
 * Performance improvements
 * API improvements
 * Added "depth" parameter to GEMM
 * Android cross-compilation
This commit is contained in:
Philippe Tillet
2015-04-29 15:50:57 -04:00
parent 5ff16bfcb6
commit cf5028d55b
3819 changed files with 7080 additions and 2916 deletions
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55
python/autotune/pysrc/genetic.py
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@@ -2,7 +2,7 @@ import random, time, sys, copy
import misc_tools
import numpy as np
import pyatidlas as atd
import pyisaac as atd
from deap import algorithms
from deap import base
from deap import creator
@@ -30,6 +30,14 @@ def b_gray_to_bin(A='00000000', endian='big'):
class GeneticOperators(object):
class Pow2(object):
def __init__(self, v):
self.value = v
@property
def decoded():
return 2**self.value
def __init__(self, symbolic, Template, out):
self.device = symbolic.context.queues[0].device
self.symbolic = symbolic
@@ -39,15 +47,15 @@ class GeneticOperators(object):
self.genome_info = {
atd.vaxpy: [3,4,4,atd.fetching_policy_type],
atd.reduction: [3,4,4,atd.fetching_policy_type],
atd.maxpy: [3,3,3,3,3,atd.fetching_policy_type],
atd.mreduction_rows: [3,3,3,3,3,atd.fetching_policy_type],
atd.mreduction_cols: [3,3,3,3,3,atd.fetching_policy_type],
atd.mproduct_nn: [3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3],
atd.mproduct_nt: [3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3],
atd.mproduct_tn: [3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3],
atd.mproduct_tt: [3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3]
atd.vaxpy: [2,4,4,atd.fetching_policy_type],
atd.reduction: [2,4,4,atd.fetching_policy_type],
atd.maxpy: [2,3,3,3,3,atd.fetching_policy_type],
atd.mreduction_rows: [2,3,3,3,3,atd.fetching_policy_type],
atd.mreduction_cols: [2,3,3,3,3,atd.fetching_policy_type],
atd.mproduct_nn: [2,3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3],
atd.mproduct_nt: [2,3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3],
atd.mproduct_tn: [2,3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3],
atd.mproduct_tt: [2,3,3,3,3,3,3,3,atd.fetching_policy_type,atd.fetching_policy_type,3]
}[Template]
self.indpb = 1.0/sum([1 if x==atd.fetching_policy_type else x for x in self.genome_info])
@@ -64,29 +72,30 @@ class GeneticOperators(object):
def decode(self, genome):
fetching_policy_type = atd.fetching_policy_type
fetch = [fetching_policy_type.FETCH_FROM_LOCAL, fetching_policy_type.FETCH_FROM_GLOBAL_STRIDED, fetching_policy_type.FETCH_FROM_GLOBAL_CONTIGUOUS]
decode_element = lambda x:2**int(b_gray_to_bin(''.join(x)), 2)
is_gemm = self.Template in [atd.mproduct_nn, atd.mproduct_nt, atd.mproduct_tn, atd.mproduct_tt]
result = []
offset = 0
for x in self.genome_info:
for i, x in enumerate(self.genome_info):
if x==atd.fetching_policy_type:
result.append(fetch[genome[offset]])
offset = offset + 1
else:
result.append(decode_element(genome[offset:offset+x]))
decoded = int(b_gray_to_bin(''.join(genome[offset:offset+x])), 2)
result.append(decoded if is_gemm and i in [11, 12] else 2**decoded)
offset = offset + x
#GEMM peculiarities
if self.Template in [atd.mproduct_nn, atd.mproduct_nt, atd.mproduct_tn, atd.mproduct_tt]:
if is_gemm:
if fetching_policy_type.FETCH_FROM_LOCAL in result:
lf1 = result[1]*result[3]/result[9]
lf1 = result[1]*result[3]/result[10]
else:
result[9] = 0
result[10] = 0
lf1 = 0
result.append(lf1)
return result
def init(self, N):
result = []
allowed_idx = [0,1,2] if self.Template in [atd.mproduct_nn, atd.mproduct_nt, atd.mproduct_tn, atd.mproduct_tt] else [1,2]
allowed_idx = [0] if self.Template in [atd.mproduct_nn, atd.mproduct_nt, atd.mproduct_tn, atd.mproduct_tt] else [1,2]
for idx in allowed_idx:
current = []
while len(current) < N/len(allowed_idx):
@@ -114,14 +123,13 @@ class GeneticOperators(object):
while True:
new_individual = copy.deepcopy(individual)
for i in range(len(new_individual)):
if isinstance(individual[i], int) and random.random() < self.indpb:
if isinstance(individual[i], int) and random.random() < 0.1:
while new_individual[i] == individual[i]:
new_individual[i] = random.randint(0, 2)
elif not isinstance(individual[i], int) and random.random() < self.indpb:
new_individual[i] = '1' if new_individual[i]=='0' else '0'
parameters = self.decode(new_individual)
template = self.Template(*parameters)
#print tools.skip(template, self.symbolic), parameters
if not misc_tools.skip(template, self.symbolic):
break
return new_individual,
@@ -130,12 +138,9 @@ class GeneticOperators(object):
if tuple(individual) not in self.cache:
parameters = self.decode(individual)
template = self.Template(*parameters)
try:
tt = misc_tools.benchmark(template, self.symbolic)
self.out.write(','.join([str(tt)]+map(str,map(int,parameters)))+'\n')
self.cache[tuple(individual)] = tt
except ValueError:
self.cache[tuple(individual)] = 10
tt = misc_tools.benchmark(template, self.symbolic)
self.out.write(','.join([str(tt)]+map(str,map(int,parameters)))+'\n')
self.cache[tuple(individual)] = tt
return self.cache[tuple(individual)],
def optimize(self, maxtime, maxgen, compute_perf, perf_metric):