gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

More robust dataset

Browse Source
This commit is contained in:
Philippe Tillet
2014-09-30 10:11:22 +02:00
parent 0a1894d003
commit 3523a3756f
5 changed files with 73 additions and 83 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
autotune/python/autotune.py
View File

@@ -101,7 +101,7 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
if 'all' in layouts:
layouts = ['NN', 'NT', 'TN', 'TT']
for layout in layouts:
def execution_handler(sizes, fname, parameters=None):
def execution_handler(sizes, fname=os.devnull, parameters=None):
A_trans = layout[0]
B_trans = layout[1]
A = vcl.Matrix((sizes[0], sizes[1]) if A_trans=='N' else (sizes[1],sizes[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
@@ -116,7 +116,7 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
TemplateType = TYPES[operation]['template']
return tools.benchmark(TemplateType(TemplateType.Parameters(*parameters),A_trans,B_trans), statement, device)
else:
execute(statement,(A_trans, B_trans), sizes, fname)
return execute(statement,(A_trans, B_trans), sizes, fname)
X, Y, profiles = generate_dataset(TYPES[operation]['template'], execution_handler)
train_model(X, Y, profiles)

146
autotune/python/dataset.py
View File

@@ -3,7 +3,7 @@ import sys
import re
import random
import numpy as np
from sklearn.neighbors.kde import KernelDensity;
from sklearn.neighbors.kde import KernelDensity
from pyviennacl.atidlas import FetchingPolicy
def decode(y):
@@ -12,91 +12,83 @@ def decode(y):
y[8] = fetch[y[8]]
return y
def resample(X, tbincount, densities, step):
Xtuples = [tuple(x) for x in X]
r = random.random()
while(True):
if(len(tbincount)==0 or len(densities)==0 or r<=1.0/len(densities)):
x = np.array([step*random.randint(1,40), step*random.randint(1,40), step*random.randint(1,40)]);
else:
probs = [1.0/x if x>0 else 0 for x in tbincount]
distr = np.random.choice(range(tbincount.size), p = probs/np.sum(probs))
x = densities[distr].sample()[0]
x = np.maximum(np.ones(x.shape),(x - step/2).astype(int)/step + 1)*step
if tuple(x) not in Xtuples:
break
return x.astype(int)
def generate_dataset(TemplateType, execution_handler):
I = 2
step = 64;
max_size = 4000;
#Retrieves the existing data
print "Retrieving data..."
I = 0
step = 64
max_size = 4000
path = "./data"
files = os.listdir(path)
X = np.empty((len(files),3))
t = np.empty(len(files))
profiles = []
nonemptyfiles = []
for i,fname in enumerate(files):
if os.path.getsize(os.path.join(path,fname))>0:
nonemptyfiles.append(fname)
files = nonemptyfiles
for i,fname in enumerate(files):
MNK = re.search(r"([0-9]+)-([0-9]+)-([0-9]+).csv", fname)
fl = open(os.path.join(path,fname),"rb")
A = np.loadtxt(fl,delimiter=',')
x = np.array([MNK.group(1), MNK.group(2), MNK.group(3)]).astype(float)
y = tuple(A[np.argmin(A[:,0]),1:])
if y not in profiles:
profiles.append(y)
idx = profiles.index(y)
X[i,:] = x
t[i] = idx
#Tries to resume
try:
X = np.loadtxt(open(os.path.join(path, "X.csv"),"rb"))
t = np.loadtxt(open(os.path.join(path, "t.csv"),"rb"))
profiles = np.loadtxt(open(os.path.join(path, "profiles.csv"),"rb")).tolist()
if not isinstance(profiles[0], list):
profiles = [profiles]
N = t.size
X.resize((N+I, 3), refcheck=False)
t.resize(N+I, refcheck=False)
print 'Resuming dataset generation...'
except:
X = np.empty((I,I))
t = np.empty(I)
profiles = []
N = 0
pass
#Generates new data
print "Generating new data..."
kdes = [KernelDensity(kernel='gaussian', bandwidth=2*step).fit(X[t==i,:]) for i in range(int(max(t))+1)] if files else [];
X.resize((len(files)+I, 3), refcheck=False);
t.resize(len(files)+I, refcheck=False);
print "Getting some good profiles..."
densities = [KernelDensity(kernel='gaussian', bandwidth=2*step).fit(X[t==i,:]) for i in range(int(max(t))+1)] if N else [];
X.resize((N+I, 3), refcheck=False)
t.resize(N+I, refcheck=False)
max_square = max_size/step
for i in range(I):
n_per_label = np.bincount(t[0:i+1].astype(int));
Xtuples = [tuple(x) for x in X];
r = random.random();
while(True):
if(len(kdes)==0 or r<=1.0/len(kdes)):
x = np.array([step*random.randint(1,40), step*random.randint(1,40), step*random.randint(1,40)]);
else:
probs = (1.0/n_per_label)
distr = np.random.choice(range(n_per_label.size), p = probs/np.sum(probs))
x = kdes[distr].sample()[0]
x = np.maximum(np.ones(x.shape),(x - step/2).astype(int)/step + 1)*step
if tuple(x) not in Xtuples:
break;
x = x.astype(int)
x = [1536,1536,1536]
fname = os.path.join(path, `x[0]` +"-"+ `x[1]` +"-"+ `x[2]` +".csv")
#Execute auto-tuning procedure
execution_handler(x, fname)
#Load csv into matrix
fl = open(fname,"rb");
A = np.loadtxt(fl,delimiter=',');
#Update the kernel density estimators
y = tuple(A[np.argmin(A[:,0]),1:]);
tbincount = np.bincount(t[0:i+1].astype(int))
x = resample(X, tbincount, densities, step)
y = execution_handler(x)
if y not in profiles:
profiles.append(y);
kdes.append(KernelDensity(kernel='gaussian', bandwidth=2*step));
idx = profiles.index(y);
#Update data
X[len(files)+i,:] = x;
t[len(files)+i] = idx;
#Update density estimator p(M,N,K | t=idx)
kdes[idx].fit(X[t[0:len(files)+i+1]==idx,:]);
profiles.append(y)
densities.append(KernelDensity(kernel='gaussian', bandwidth=2*step))
idx = profiles.index(y)
X[N+i,:] = x
t[N+i] = idx
densities[idx].fit(X[t[0:N+i+1]==idx,:])
np.savetxt(os.path.join(path,"X.csv"), X)
np.savetxt(os.path.join(path,"t.csv"), t)
np.savetxt(os.path.join(path,"profiles.csv"), profiles)
print "Exporting data...";
#Shuffle the list of file
files = os.listdir(path)
X = np.empty((len(files),3))
Y = np.zeros((len(files), len(profiles)))
for i,fname in enumerate(files):
MNK = re.search(r"([0-9]+)-([0-9]+)-([0-9]+).csv", fname)
X[i,:] = map(float,[MNK.group(k) for k in range(1,4)])
fl = open(os.path.join(path,fname),"rb");
A = np.loadtxt(fl,delimiter=',')
print "Generating the dataset..."
N = 500
Y = np.empty((N, len(profiles)))
X = np.empty((N,3))
t = []
for i in range(N):
x = resample(X, np.bincount(t), densities, step)
for j,y in enumerate(profiles):
idx = np.where(np.all(A[:,1:]==y,axis=1))[0]
T = A[idx[0], 0] if idx.size else execution_handler(map(int,X[i,:]), '', decode(map(int, y)))
Y[i,j] = 2*1e-9*X[i,0]*X[i,1]*X[i,2]/T
T = execution_handler(x, os.devnull, decode(map(int, y)))
Y[i,j] = 2*1e-9*x[0]*x[1]*x[2]/T
idx = np.argmax(Y[i,:])
X[i,:] = x
t = np.argmax(Y[:i+1,], axis=1)
densities[idx].fit(X[t==idx,:])
np.savetxt(os.path.join(path,"Y.csv"), Y)
return X, Y, profiles

2
autotune/python/genetic.py
View File

@@ -181,4 +181,4 @@ class GeneticOperators(object):
sys.stdout.write('Time %d | Best %d %s [ for %s ]\r'%(time.time() - start_time, best_performance, perf_metric, best_profile))
sys.stdout.flush()
sys.stdout.write('\n')
return population
return self.decode(hof[0])

2
autotune/python/model.py
View File

@@ -9,8 +9,6 @@ def train_model(X, Y, profiles):
X = scaler.transform(X);
ref = np.argmax(np.bincount(np.argmax(Y, axis=1))) #most common profile
print Y
print np.bincount(np.argmax(Y, axis=1))
#Cross-validation data-sets
cut = int(0.5*X.shape[0]+1);
XTr = X[0:cut, :];

2
autotune/python/optimize.py
View File

@@ -50,4 +50,4 @@ from genetic import GeneticOperators
def genetic(statement, context, TemplateType, build_template, parameter_names, compute_perf, perf_metric, out):
GA = GeneticOperators(context.devices[0], statement, parameter_names, TemplateType, build_template, out)
GA.optimize(maxtime='2m30s', maxgen=1000, compute_perf=compute_perf, perf_metric=perf_metric)
return GA.optimize(maxtime='2m30s', maxgen=1000, compute_perf=compute_perf, perf_metric=perf_metric)