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提交 1d127d63 authored 作者: James Bergstra's avatar James Bergstra
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Re-ordered blas tests so that ger is not in middle of gemv

上级 91343323
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正在显示 包含 196 行增加 和 187 行删除
theano/tensor/tests/test_blas.py
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...@@ -758,6 +758,10 @@ def test_dot_w_self(): ...@@ -758,6 +758,10 @@ def test_dot_w_self():
f(numpy.asarray([[0,1], [2,3]], dtype=config.floatX)) f(numpy.asarray([[0,1], [2,3]], dtype=config.floatX))
###############################################################################
## Tests for Gemv
###############################################################################
class TestGemv(TestCase): class TestGemv(TestCase):
def test_dot_vm(self): def test_dot_vm(self):
''' Test vector dot matrix ''' ''' Test vector dot matrix '''
...@@ -878,6 +882,198 @@ class TestGemv(TestCase): ...@@ -878,6 +882,198 @@ class TestGemv(TestCase):
self.assertRaises(ValueError, f, A_val, ones_3, ones_6) self.assertRaises(ValueError, f, A_val, ones_3, ones_6)
self.assertRaises(ValueError, f, A_val, ones_4, ones_6) self.assertRaises(ValueError, f, A_val, ones_4, ones_6)
# The following gemv tests were added in March 2011 by Ian Goodfellow
# and are based on the gemv tests from scipy
# http://projects.scipy.org/scipy/browser/trunk/scipy/linalg/tests/test_fblas.py?rev=6803
# NOTE: At the time these tests were written, theano did not have a
# conjugate function. If such a thing is ever added, the tests involving
# conjugate should be ported over as well.
def matrixmultiply(a, b):
if len(b.shape) == 1:
b_is_vector = True
b = b[:,newaxis]
else:
b_is_vector = False
assert_(a.shape[1] == b.shape[0])
c = zeros((a.shape[0], b.shape[1]), common_type(a, b))
for i in xrange(a.shape[0]):
for j in xrange(b.shape[1]):
s = 0
for k in xrange(a.shape[1]):
s += a[i,k] * b[k, j]
c[i,j] = s
if b_is_vector:
c = c.reshape((a.shape[0],))
return c
class BaseGemv(object):
def get_data(self,x_stride=1,y_stride=1):
rng = numpy.random.RandomState(unittest_tools.fetch_seed())
mult = array(1, dtype = self.dtype)
if self.dtype in [complex64,complex128]:
mult = array(1+1j, dtype = self.dtype)
alpha = array(1., dtype = self.dtype) * mult
beta = array(1., dtype = self.dtype) * mult
a = rng.randn(3,3).astype(self.dtype) * mult
x = arange(shape(a)[0]*x_stride,dtype=self.dtype) * mult
y = arange(shape(a)[1]*y_stride,dtype=self.dtype) * mult
return alpha,beta,a,x,y
def test_simple(self):
alpha, beta, a, x, y = [ shared(value) for value in self.get_data() ]
desired_oy = alpha.get_value() * matrixmultiply(a.get_value(),x.get_value()) + beta.get_value() * y.get_value()
oy = alpha * T.dot(a,x) + beta * y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_val = oy_func()
assert_array_almost_equal(desired_oy, oy_val)
def test_default_beta_y(self):
vs = self.get_data()
alpha_v, beta_v, a_v, x_v, y_v = vs
a = shared(a_v)
x = shared(x_v)
desired_oy = matrixmultiply(a_v, x_v)
oy = T.dot(a,x)
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
# The only op in the graph is a dot.
# In the gemm case, we create a dot22 for that case
# There is no dot21.
# Creating one is not usefull as this is not faster(in fact it would be slower!
# as more code would be in python, numpy.dot will call gemv itself)
# See ticket 594
"""
>>> t0=time.time();x=scipy.linalg.blas.fblas.dgemv(1,a.T,b,1,z.T);t1=time.time();print t1-t0
0.00192999839783
>>> t0=time.time();x=numpy.dot(a,b);t1=time.time();print t1-t0
0.00158381462097
"""
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==0
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_simple_transpose(self):
vs = self.get_data()
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(transpose(a_v),x_v)+beta_v*y_v
oy = alpha * T.dot(a.T,x)+beta*y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_x_stride(self):
vs = self.get_data(x_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(a_v,x_v[::2])+beta_v*y_v
oy = alpha * T.dot(a,x[::2])+beta*y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_x_stride_transpose(self):
vs = self.get_data(x_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(transpose(a_v),x_v[::2])+beta_v*y_v
oy = alpha * T.dot(a.T,x[::2])+beta*y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_y_stride(self):
vs = self.get_data(y_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(a_v,x_v)+beta_v*y_v[::2]
oy = alpha * T.dot(a,x)+beta*y[::2]
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_y_stride_transpose(self):
vs = self.get_data(y_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(transpose(a_v),x_v)+beta_v*y_v[::2]
oy = alpha * T.dot(a.T,x)+beta*y[::2]
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
class TestSgemv(TestCase, BaseGemv):
dtype = float32
class TestDgemv(TestCase, BaseGemv):
dtype = float64
#The optimization to put Gemv don't work for complex type for now.
# See ticket 653.
#class TestCgemv(TestCase, BaseGemv):
# dtype = complex64
#class TestZgemv(TestCase, BaseGemv):
# dtype = complex128
###############################################################################
## Tests for Ger
###############################################################################
class TestGer_make_node(TestCase): class TestGer_make_node(TestCase):
def setUp(self): def setUp(self):
self.iv = T.tensor(dtype='int32', broadcastable=(False,)) self.iv = T.tensor(dtype='int32', broadcastable=(False,))
...@@ -1067,190 +1263,3 @@ class TestGer_make_thunk(TestCase): ...@@ -1067,190 +1263,3 @@ class TestGer_make_thunk(TestCase):
def test_c128_1_9(s): return s.given_dtype('complex128', 1, 9) def test_c128_1_9(s): return s.given_dtype('complex128', 1, 9)
# The following gemv tests were added in March 2011 by Ian Goodfellow
# and are based on the gemv tests from scipy
# http://projects.scipy.org/scipy/browser/trunk/scipy/linalg/tests/test_fblas.py?rev=6803
# NOTE: At the time these tests were written, theano did not have a
# conjugate function. If such a thing is ever added, the tests involving
# conjugate should be ported over as well.
def matrixmultiply(a, b):
if len(b.shape) == 1:
b_is_vector = True
b = b[:,newaxis]
else:
b_is_vector = False
assert_(a.shape[1] == b.shape[0])
c = zeros((a.shape[0], b.shape[1]), common_type(a, b))
for i in xrange(a.shape[0]):
for j in xrange(b.shape[1]):
s = 0
for k in xrange(a.shape[1]):
s += a[i,k] * b[k, j]
c[i,j] = s
if b_is_vector:
c = c.reshape((a.shape[0],))
return c
class BaseGemv(object):
def get_data(self,x_stride=1,y_stride=1):
rng = numpy.random.RandomState(unittest_tools.fetch_seed())
mult = array(1, dtype = self.dtype)
if self.dtype in [complex64,complex128]:
mult = array(1+1j, dtype = self.dtype)
alpha = array(1., dtype = self.dtype) * mult
beta = array(1., dtype = self.dtype) * mult
a = rng.randn(3,3).astype(self.dtype) * mult
x = arange(shape(a)[0]*x_stride,dtype=self.dtype) * mult
y = arange(shape(a)[1]*y_stride,dtype=self.dtype) * mult
return alpha,beta,a,x,y
def test_simple(self):
alpha, beta, a, x, y = [ shared(value) for value in self.get_data() ]
desired_oy = alpha.get_value() * matrixmultiply(a.get_value(),x.get_value()) + beta.get_value() * y.get_value()
oy = alpha * T.dot(a,x) + beta * y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_val = oy_func()
assert_array_almost_equal(desired_oy, oy_val)
def test_default_beta_y(self):
vs = self.get_data()
alpha_v, beta_v, a_v, x_v, y_v = vs
a = shared(a_v)
x = shared(x_v)
desired_oy = matrixmultiply(a_v, x_v)
oy = T.dot(a,x)
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
# The only op in the graph is a dot.
# In the gemm case, we create a dot22 for that case
# There is no dot21.
# Creating one is not usefull as this is not faster(in fact it would be slower!
# as more code would be in python, numpy.dot will call gemv itself)
# See ticket 594
"""
>>> t0=time.time();x=scipy.linalg.blas.fblas.dgemv(1,a.T,b,1,z.T);t1=time.time();print t1-t0
0.00192999839783
>>> t0=time.time();x=numpy.dot(a,b);t1=time.time();print t1-t0
0.00158381462097
"""
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==0
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_simple_transpose(self):
vs = self.get_data()
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(transpose(a_v),x_v)+beta_v*y_v
oy = alpha * T.dot(a.T,x)+beta*y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_x_stride(self):
vs = self.get_data(x_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(a_v,x_v[::2])+beta_v*y_v
oy = alpha * T.dot(a,x[::2])+beta*y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_x_stride_transpose(self):
vs = self.get_data(x_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(transpose(a_v),x_v[::2])+beta_v*y_v
oy = alpha * T.dot(a.T,x[::2])+beta*y
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_y_stride(self):
vs = self.get_data(y_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(a_v,x_v)+beta_v*y_v[::2]
oy = alpha * T.dot(a,x)+beta*y[::2]
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
def test_y_stride_transpose(self):
vs = self.get_data(y_stride = 2)
alpha_v, beta_v, a_v, x_v, y_v = vs
alpha, beta, a, x, y = [ shared(v) for v in vs ]
desired_oy = alpha_v * matrixmultiply(transpose(a_v),x_v)+beta_v*y_v[::2]
oy = alpha * T.dot(a.T,x)+beta*y[::2]
oy_func = theano.function([], oy, mode = mode_blas_opt)
topo = oy_func.maker.env.toposort()
assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
oy_v = oy_func()
assert_array_almost_equal(desired_oy, oy_v)
class TestSgemv(TestCase, BaseGemv):
dtype = float32
class TestDgemv(TestCase, BaseGemv):
dtype = float64
#The optimization to put Gemv don't work for complex type for now.
# See ticket 653.
#class TestCgemv(TestCase, BaseGemv):
# dtype = complex64
#class TestZgemv(TestCase, BaseGemv):
# dtype = complex128
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