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pytensor
提交 da378de8 authored 作者: Frederic's avatar Frederic
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Refactor test to have more tests run on the GPU.

上级 91739a95
隐藏空白字符变更
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正在显示 包含 95 行增加 和 208 行删除
theano/sandbox/gpuarray/tests/test_elemwise.py
浏览文件 @ da378de8
......@@ -2,7 +2,7 @@ from theano import scalar, gof
from theano.gof.python25 import all, any
from theano.tensor.tests.test_elemwise import (test_Broadcast, test_DimShuffle,
test_CAReduce, T_sum_dtype)
test_CAReduce, T_reduce_dtype)
from theano.sandbox.gpuarray.tests.test_basic_ops import (mode_with_gpu,
rand_gpuarray)
......@@ -152,7 +152,7 @@ class test_GpuCAReduceCuda(test_GpuCAReduceCPY):
return
class T_gpusum_dtype(T_sum_dtype):
class T_gpureduce_dtype(T_reduce_dtype):
mode = mode_with_gpu.excluding('local_cut_useless_reduce')
op = GpuCAReduceCuda
#Currently we don't support reduction on 0 axis
......
theano/tensor/tests/test_elemwise.py
浏览文件 @ da378de8
......@@ -716,44 +716,47 @@ class test_IsInf_IsNan(unittest.TestCase):
return self.run_isfunc('isnan')
class T_sum_dtype(unittest.TestCase):
class T_reduce_dtype(unittest.TestCase):
mode = theano.compile.get_default_mode().excluding(
'local_cut_useless_reduce')
op = CAReduce
axes = [None, 0, 1, [], [0], [1], [0, 1]]
methods = ['sum', 'prod']
def test_sum_default_dtype(self):
def test_reduce_default_dtype(self):
"""
Test the default dtype of a sum().
Test the default dtype of a method().
"""
# We try multiple axis combinations even though axis should not matter.
for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
axis = self.axes[idx % len(self.axes)]
x = tensor.matrix(dtype=dtype)
s = x.sum(axis=axis)
assert s.dtype == dict(
for method in self.methods:
for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
axis = self.axes[idx % len(self.axes)]
x = tensor.matrix(dtype=dtype)
s = getattr(x, method)(axis=axis)
assert s.dtype == dict(
int8='int64',
int16='int64',
int32='int64',
uint8='uint64',
uint16='uint64',
uint32='uint64',
).get(dtype, dtype)
f = theano.function([x], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
).get(dtype, dtype)
f = theano.function([x], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
def test_sum_default_acc_dtype(self):
##Test the default acc_dtype of a sum().
def test_reduce_default_acc_dtype(self):
##Test the default acc_dtype of a reduce().
# We try multiple axis combinations even though axis should not matter.
for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
axis = self.axes[idx % len(self.axes)]
x = tensor.matrix(dtype=dtype)
s = x.sum(axis=axis)
assert s.owner.op.acc_dtype == dict(
for method in self.methods:
for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
axis = self.axes[idx % len(self.axes)]
x = tensor.matrix(dtype=dtype)
s = getattr(x, method)(axis=axis)
assert s.owner.op.acc_dtype == dict(
int8='int64',
int16='int64',
int32='int64',
......@@ -762,95 +765,102 @@ class T_sum_dtype(unittest.TestCase):
uint32='uint64',
float32='float64',
complex64='complex128',
).get(dtype, dtype)
f = theano.function([x], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
).get(dtype, dtype)
f = theano.function([x], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
@attr('slow')
def test_sum_custom_dtype(self):
def test_reduce_custom_dtype(self):
"""
Test the ability to provide your own output dtype for a sum.
Test the ability to provide your own output dtype for a reduce.
"""
# We try multiple axis combinations even though axis should not matter.
idx = 0
for input_dtype in imap(str, theano.scalar.all_types):
x = tensor.matrix(dtype=input_dtype)
for output_dtype in imap(str, theano.scalar.all_types):
# If the output is a complex, the gradient of the sum will
for method in self.methods:
for input_dtype in imap(str, theano.scalar.all_types):
x = tensor.matrix(dtype=input_dtype)
for output_dtype in imap(str, theano.scalar.all_types):
# If the output is a complex, the gradient of the reduce will
# cast the complex to the input dtype. We can't call the normal
# cast on a complex to a not complex as this is ambiguous.
if (not input_dtype.startswith('complex') and
output_dtype.startswith('complex')):
continue
if (not input_dtype.startswith('complex') and
output_dtype.startswith('complex')):
continue
axis = self.axes[idx % len(self.axes)]
sum_var = x.sum(dtype=output_dtype, axis=axis)
assert sum_var.dtype == output_dtype
axis = self.axes[idx % len(self.axes)]
var = getattr(x, method)(dtype=output_dtype, axis=axis)
assert var.dtype == output_dtype
f = theano.function([x], sum_var, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(input_dtype)
f(data)
if "complex" in input_dtype:
continue
# Check that we can take the gradient
tensor.grad(sum_var.sum(), x,
disconnected_inputs='ignore')
idx += 1
f = theano.function([x], var, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(input_dtype)
f(data)
if "complex" in input_dtype:
continue
# Check that we can take the gradient
tensor.grad(var.sum(), x,
disconnected_inputs='ignore')
idx += 1
def test_sum_custom_acc_dtype(self):
def test_reduce_custom_acc_dtype(self):
"""
Test the ability to provide your own accumulator dtype for a sum.
Test the ability to provide your own accumulator dtype for a reduce.
"""
# We try multiple axis combinations even though axis should not matter.
idx = 0
for input_dtype in imap(str, theano.scalar.all_types):
x = tensor.matrix(dtype=input_dtype)
for acc_dtype in imap(str, theano.scalar.all_types):
# If the accumulator is a complex, the gradient of the sum will
for method in self.methods:
for input_dtype in imap(str, theano.scalar.all_types):
x = tensor.matrix(dtype=input_dtype)
for acc_dtype in imap(str, theano.scalar.all_types):
# If the accumulator is a complex, the gradient of the reduce will
# cast the complex to the input dtype. We can't call the normal
# cast on a complex to a not complex as this is ambiguous.
if (not input_dtype.startswith('complex') and
acc_dtype.startswith('complex')):
continue
if (not input_dtype.startswith('complex') and
acc_dtype.startswith('complex')):
continue
axis = self.axes[idx % len(self.axes)]
axis = self.axes[idx % len(self.axes)]
# If output_dtype would force a downcast, we expect a TypeError
# We always allow int/uint inputs with float/complex outputs.
upcasted_dtype = scalar.upcast(input_dtype, acc_dtype)
if (acc_dtype == upcasted_dtype or
upcasted_dtype = scalar.upcast(input_dtype, acc_dtype)
if (acc_dtype == upcasted_dtype or
(input_dtype in tensor.discrete_dtypes and
acc_dtype in tensor.continuous_dtypes)
):
sum_var = x.sum(acc_dtype=acc_dtype, axis=axis)
assert sum_var.owner.op.acc_dtype == acc_dtype
var = getattr(x, method)(acc_dtype=acc_dtype, axis=axis)
assert var.owner.op.acc_dtype == acc_dtype
if "complex" in input_dtype:
continue
if "complex" in input_dtype:
continue
# Check that we can take the gradient
tensor.grad(sum_var.sum(), x,
disconnected_inputs='ignore')
else:
self.assertRaises(TypeError,
x.sum, acc_dtype=acc_dtype, axis=axis)
tensor.grad(var.sum(), x,
disconnected_inputs='ignore')
else:
self.assertRaises(TypeError,
getattr(x, method),
acc_dtype=acc_dtype, axis=axis)
idx += 1
idx += 1
def test_sum_precision(self):
def test_reduce_precision(self):
# Check that the default accumulator precision is sufficient
x = theano.shared(numpy.asarray([1e8, 1, -1e8], dtype='float32'))
s = x.sum()
f = theano.function([], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
s_val = f()
assert numpy.allclose(s_val, 1)
for method in self.methods:
x = theano.shared(numpy.asarray([1e8, 1, -1e8],
dtype='float32'))
s = getattr(x, method)()
f = theano.function([], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo, dtype)
s_val = f()
# Use extra precision in NumPy to compute the good answer.
ret = getattr(numpy.asarray([1e8, 1, -1e8], dtype='float64'), method)()
assert numpy.allclose(s_val, ret), (s_val, ret)
class T_mean_dtype(unittest.TestCase):
......@@ -932,129 +942,6 @@ class T_mean_dtype(unittest.TestCase):
assert numpy.allclose(m_val, 1. / 3)
class T_prod_dtype(unittest.TestCase):
def test_prod_default_dtype(self):
"""
Test the default dtype of a prod().
"""
# We try multiple axis combinations even though axis should not matter.
axes = [None, 0, 1, [], [0], [1], [0, 1]]
for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
axis = axes[idx % len(axes)]
x = tensor.matrix(dtype=dtype)
p = x.prod(axis=axis)
assert p.dtype == dict(
int8='int64',
int16='int64',
int32='int64',
uint8='uint64',
uint16='uint64',
uint32='uint64',
).get(dtype, dtype)
f = theano.function([x], p)
data = numpy.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
def test_prod_default_acc_dtype(self):
"""
Test the default acc_dtype of a prod().
"""
# We try multiple axis combinations even though axis should not matter.
axes = [None, 0, 1, [], [0], [1], [0, 1]]
for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
axis = axes[idx % len(axes)]
x = tensor.matrix(dtype=dtype)
p = x.prod(axis=axis)
assert p.owner.op.acc_dtype == dict(
int8='int64',
int16='int64',
int32='int64',
uint8='uint64',
uint16='uint64',
uint32='uint64',
float32='float64',
complex64='complex128',
).get(dtype, dtype)
f = theano.function([x], p)
data = numpy.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
@attr('slow')
def test_prod_custom_dtype(self):
"""
Test the ability to provide your own output dtype for a prod.
"""
# We try multiple axis combinations even though axis should not matter.
axes = [None, 0, 1, [], [0], [1], [0, 1]]
idx = 0
for input_dtype in imap(str, theano.scalar.all_types):
x = tensor.matrix(dtype=input_dtype)
for output_dtype in imap(str, theano.scalar.all_types):
axis = axes[idx % len(axes)]
idx += 1
prod_var = x.prod(dtype=output_dtype, axis=axis)
assert prod_var.dtype == output_dtype
if (('complex' in output_dtype or
'complex' in input_dtype) and
input_dtype != output_dtype):
continue
f = theano.function([x], prod_var)
data = numpy.random.rand(3, 4) * 10
data = data.astype(input_dtype)
f(data)
if "complex" in output_dtype or "complex" in input_dtype:
continue
# Check that we can take the gradient
tensor.grad(prod_var.sum(), x,
disconnected_inputs='ignore')
@attr('slow')
def test_prod_custom_acc_dtype(self):
"""
Test the ability to provide your own acc_dtype for a prod.
"""
# We try multiple axis combinations even though axis should not matter.
axes = [None, 0, 1, [], [0], [1], [0, 1]]
idx = 0
for input_dtype in imap(str, theano.scalar.all_types):
x = tensor.matrix(dtype=input_dtype)
for acc_dtype in imap(str, theano.scalar.all_types):
axis = axes[idx % len(axes)]
# If acc_dtype would force a downcast, we expect a TypeError
# We always allow int/uint inputs with float/complex outputs.
upcasted_dtype = scalar.upcast(input_dtype, acc_dtype)
if (acc_dtype == upcasted_dtype or
(input_dtype in tensor.discrete_dtypes and
acc_dtype in tensor.continuous_dtypes)
):
prod_var = x.prod(acc_dtype=acc_dtype, axis=axis)
assert prod_var.owner.op.acc_dtype == acc_dtype
if (acc_dtype.startswith('complex') and
input_dtype != acc_dtype):
continue
f = theano.function([x], prod_var)
data = numpy.random.rand(3, 4) * 10
data = data.astype(input_dtype)
f(data)
if "complex" in acc_dtype:
continue
# Check that we can take the gradient
tensor.grad(prod_var.sum(), x,
disconnected_inputs='ignore')
else:
self.assertRaises(TypeError,
x.prod, acc_dtype=acc_dtype, axis=axis)
idx += 1
class T_prod_without_zeros_dtype(unittest.TestCase):
def test_prod_without_zeros_default_dtype(self):
"""
......
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