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提交 f41011a9 authored 作者: Frederic Bastien's avatar Frederic Bastien
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moved test of rng_mrg to a separate file and fix one of the tests.

上级 aca8eb0e
隐藏空白字符变更
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正在显示 包含 155 行增加 和 151 行删除
theano/sandbox/rng_mrg.py
浏览文件 @ f41011a9
...@@ -14,9 +14,9 @@ from theano.tensor import zeros_like, sqrt, log, sin, cos, join ...@@ -14,9 +14,9 @@ from theano.tensor import zeros_like, sqrt, log, sin, cos, join
from theano.compile import optdb from theano.compile import optdb
from theano.gof import local_optimizer from theano.gof import local_optimizer
from theano.sandbox.cuda import cuda_enabled from theano.sandbox.cuda import cuda_available
if cuda_enabled: if cuda_available:
from theano.sandbox.cuda import CudaNdarrayType from theano.sandbox.cuda import CudaNdarrayType, float32_shared_constructor
def mulmod(a, b, c, m): def mulmod(a, b, c, m):
r = numpy.int32((numpy.int64(a)*b + c) % m) r = numpy.int32((numpy.int64(a)*b + c) % m)
...@@ -628,7 +628,7 @@ class MRG_RandomStreams(object): ...@@ -628,7 +628,7 @@ class MRG_RandomStreams(object):
tmp_float_buf = numpy.frombuffer(rstates.data, dtype='float32') tmp_float_buf = numpy.frombuffer(rstates.data, dtype='float32')
assert tmp_float_buf.shape == rstates.shape assert tmp_float_buf.shape == rstates.shape
assert tmp_float_buf.data[:24] == rstates.data[:24] assert tmp_float_buf.data[:24] == rstates.data[:24]
node_rstate = shared(tmp_float_buf) # transfer to device node_rstate = float32_shared_constructor(tmp_float_buf) # transfer to device
assert isinstance(node_rstate.type, CudaNdarrayType) assert isinstance(node_rstate.type, CudaNdarrayType)
# we can't use the normal mrg_uniform constructor + later optimization # we can't use the normal mrg_uniform constructor + later optimization
...@@ -706,154 +706,8 @@ def mrg_random_make_inplace(node): ...@@ -706,154 +706,8 @@ def mrg_random_make_inplace(node):
optdb.register('random_make_inplace_mrg', opt.in2out(mrg_random_make_inplace, ignore_newtrees=True), 99, 'fast_run', 'inplace') optdb.register('random_make_inplace_mrg', opt.in2out(mrg_random_make_inplace, ignore_newtrees=True), 99, 'fast_run', 'inplace')
#
#
#
#
#
import time
import theano
def test_rng0():
def basictest(f, steps, prefix=""):
dt = 0.0
for i in xrange(steps):
t0 = time.time()
ival = f()
dt += time.time() - t0
ival = numpy.asarray(ival)
if i == 0:
mean = numpy.array(ival, copy=True)
else:
alpha = 1.0 / (1+i)
mean = alpha * ival + (1-alpha)*mean
print prefix, 'mean', numpy.mean(mean)
assert abs(numpy.mean(mean) - 0.5) < .01, 'bad mean?'
print prefix, 'time', dt
print prefix, 'elements', steps*sample_size[0]*sample_size[1]
print prefix, 'samples/sec', steps*sample_size[0]*sample_size[1] / dt
if 0:
mean, std, min, max = numpy.mean(l), numpy.std(l), numpy.min(l), numpy.max(l)
print prefix, 'mean', mean
print prefix, 'std', std
print prefix, 'min', repr(min)
print prefix, 'max', repr(max)
assert max < 1.0
assert min >= 0.0
assert abs(mean - 0.5) < .01, 'bad mean?'
sample_size = (1000,100)
print ''
print 'ON CPU:'
R = MRG_RandomStreams(234, use_cuda=False)
u = R.uniform(size=sample_size)
f = theano.function([], u)
theano.printing.debugprint(f)
print 'random?[:10]\n', f()[0,0:10]
basictest(f, 1000, prefix='mrg ')
print ''
print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
u = R.uniform(size=sample_size)
assert u.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
f = theano.function([], theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True))
theano.printing.debugprint(f)
print 'random?[:10]\n', numpy.asarray(f())[0,0:10]
basictest(f, 1000, prefix='mrg ')
print ''
print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.uniform(size=sample_size)
ff = theano.function([], uu)
basictest(ff, 1000, prefix='numpy')
def test_normal0():
def basictest(f, steps, target_avg, target_std, prefix=""):
dt = 0.0
avg_std = 0.0
for i in xrange(steps):
t0 = time.time()
ival = f()
dt += time.time() - t0
ival = numpy.asarray(ival)
if i == 0:
mean = numpy.array(ival, copy=True)
avg_std = numpy.std(ival)
else:
alpha = 1.0 / (1+i)
mean = alpha * ival + (1-alpha)*mean
avg_std = alpha * numpy.std(ival) + (1-alpha)*avg_std
print prefix, 'mean', numpy.mean(mean)
assert abs(numpy.mean(mean) - target_avg) < .01, 'bad mean?'
print prefix, 'std', avg_std
assert abs(avg_std - target_std) < .01, 'bad std?'
print prefix, 'time', dt
print prefix, 'elements', steps*sample_size[0]*sample_size[1]
print prefix, 'samples/sec', steps*sample_size[0]*sample_size[1] / dt
sample_size = (999,100)
print ''
print 'ON CPU:'
R = MRG_RandomStreams(234, use_cuda=False)
n = R.normal(size=sample_size, avg=-5.0, std=2.0)
f = theano.function([], n)
theano.printing.debugprint(f)
print 'random?[:10]\n', f()[0,0:10]
basictest(f, 50, -5.0, 2.0, prefix='mrg ')
sys.stdout.flush()
# now with odd number of samples
sample_size = (999,99)
print ''
print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
n = R.normal(size=sample_size, avg=-5.0, std=2.0, dtype='float32')
assert n.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
f = theano.function([], theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
borrow=True))
theano.printing.debugprint(f)
sys.stdout.flush()
print 'random?[:10]\n', numpy.asarray(f())[0,0:10]
print '----'
sys.stdout.flush()
basictest(f, 50, -5.0, 2.0, prefix='gpu mrg ')
print ''
print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
nn = RR.normal(size=sample_size, avg=-5.0, std=2.0)
ff = theano.function([], nn)
basictest(ff, 50, -5.0, 2.0, prefix='numpy ')
if __name__ == '__main__': if __name__ == '__main__':
# with: export THEANO_FLAGS=device=gpu0,floatX=float32 # with: export THEANO_FLAGS=device=gpu0,floatX=float32
test_normal0() #test_normal0()
theano/sandbox/test_rng_mrg.py 0 → 100644
浏览文件 @ f41011a9
import sys, time
import numpy
import theano
from theano.sandbox.rng_mrg import MRG_RandomStreams
#TODO: test gpu
#TODO: test MRG_RandomStreams
#TODO: test optimizer mrg_random_make_inplace
#def test_rng_mrg_cpu():
#TODO: make tests work when no flags gived. Now need: THEANO_FLAGS=device=gpu0,floatX=float32
#TODO: bug fix test_normal0, in normal() fct, n_samples currently need to be numpy.prod(size) not self.n_streams(size)
def test_rng0():
def basictest(f, steps, prefix=""):
dt = 0.0
for i in xrange(steps):
t0 = time.time()
ival = f()
dt += time.time() - t0
ival = numpy.asarray(ival)
if i == 0:
mean = numpy.array(ival, copy=True)
else:
alpha = 1.0 / (1+i)
mean = alpha * ival + (1-alpha)*mean
assert ival.min()>0 and ival.max()<1
print prefix, 'mean', numpy.mean(mean)
assert abs(numpy.mean(mean) - 0.5) < .01, 'bad mean?'
print prefix, 'time', dt
print prefix, 'elements', steps*sample_size[0]*sample_size[1]
print prefix, 'samples/sec', steps*sample_size[0]*sample_size[1] / dt
if 0:
mean, std, min, max = numpy.mean(l), numpy.std(l), numpy.min(l), numpy.max(l)
print prefix, 'mean', mean
print prefix, 'std', std
print prefix, 'min', repr(min)
print prefix, 'max', repr(max)
assert max < 1.0
assert min >= 0.0
assert abs(mean - 0.5) < .01, 'bad mean?'
sample_size = (1000,100)
print ''
print 'ON CPU:'
R = MRG_RandomStreams(234, use_cuda=False)
u = R.uniform(size=sample_size)
f = theano.function([], u)
theano.printing.debugprint(f)
print 'random?[:10]\n', f()[0,0:10]
basictest(f, 1000, prefix='mrg ')
print ''
print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
u = R.uniform(size=sample_size, dtype='float32')
assert u.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
f = theano.function([], theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True))
theano.printing.debugprint(f)
print 'random?[:10]\n', numpy.asarray(f())[0,0:10]
basictest(f, 1000, prefix='mrg ')
print ''
print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.uniform(size=sample_size)
ff = theano.function([], uu)
basictest(ff, 1000, prefix='numpy')
def test_normal0():
def basictest(f, steps, target_avg, target_std, prefix=""):
dt = 0.0
avg_std = 0.0
for i in xrange(steps):
t0 = time.time()
ival = f()
dt += time.time() - t0
ival = numpy.asarray(ival)
if i == 0:
mean = numpy.array(ival, copy=True)
avg_std = numpy.std(ival)
else:
alpha = 1.0 / (1+i)
mean = alpha * ival + (1-alpha)*mean
avg_std = alpha * numpy.std(ival) + (1-alpha)*avg_std
print prefix, 'mean', numpy.mean(mean)
assert abs(numpy.mean(mean) - target_avg) < .01, 'bad mean?'
print prefix, 'std', avg_std
assert abs(avg_std - target_std) < .01, 'bad std?'
print prefix, 'time', dt
print prefix, 'elements', steps*sample_size[0]*sample_size[1]
print prefix, 'samples/sec', steps*sample_size[0]*sample_size[1] / dt
sample_size = (999,100)
print ''
print 'ON CPU:'
R = MRG_RandomStreams(234, use_cuda=False)
n = R.normal(size=sample_size, avg=-5.0, std=2.0)
f = theano.function([], n)
theano.printing.debugprint(f)
print 'random?[:10]\n', f()[0,0:10]
basictest(f, 50, -5.0, 2.0, prefix='mrg ')
sys.stdout.flush()
# now with odd number of samples
sample_size = (999,99)
print ''
print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
n = R.normal(size=sample_size, avg=-5.0, std=2.0, dtype='float32')
assert n.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
f = theano.function([], theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
borrow=True))
theano.printing.debugprint(f)
sys.stdout.flush()
print 'random?[:10]\n', numpy.asarray(f())[0,0:10]
print '----'
sys.stdout.flush()
basictest(f, 50, -5.0, 2.0, prefix='gpu mrg ')
print ''
print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
nn = RR.normal(size=sample_size, avg=-5.0, std=2.0)
ff = theano.function([], nn)
basictest(ff, 50, -5.0, 2.0, prefix='numpy ')
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