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提交 2c7949b6 authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Merge pull request #1526 from nouiz/lamblin-fix_pickle_cache_leak2

fix pickle cache leak
上级 906184b6 741e80b3
隐藏空白字符变更
内嵌 并排
正在显示 包含 149 行增加 和 129 行删除
theano/gof/tests/test_compute_test_value.py
浏览文件 @ 2c7949b6
......@@ -14,6 +14,30 @@ from theano.scan_module import scan
from theano.tensor.basic import _allclose
# Used in TestComputeTestValue.test_no_perform
class IncOneC(Op):
"""An Op with only a C (c_code) implementation"""
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, input):
input = scalar.as_scalar(input)
output = input.type()
return Apply(self, [input], [output])
def c_code_cache_version(self):
return (1,)
def c_code(self, node, name, inputs, outputs, sub):
x, = inputs
z, = outputs
return "%(z)s = %(x)s + 1;" % locals()
class TestComputeTestValue(unittest.TestCase):
def test_variable_only(self):
......@@ -338,28 +362,6 @@ class TestComputeTestValue(unittest.TestCase):
def test_no_perform(self):
if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.")
class IncOneC(Op):
"""An Op with only a C (c_code) implementation"""
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, input):
input = scalar.as_scalar(input)
output = input.type()
return Apply(self, [input], [output])
def c_code_cache_version(self):
return (1,)
def c_code(self, node, name, inputs, outputs, sub):
x, = inputs
z, = outputs
return "%(z)s = %(x)s + 1;" % locals()
orig_compute_test_value = theano.config.compute_test_value
try:
......@@ -368,6 +370,8 @@ class TestComputeTestValue(unittest.TestCase):
i = scalar.int32('i')
i.tag.test_value = 3
# Class IncOneC is defined outside of the TestComputeTestValue
# so it can be pickled and unpickled
o = IncOneC()(i)
# Check that the perform function is not implemented
......
theano/scalar/basic.py
浏览文件 @ 2c7949b6
......@@ -24,7 +24,7 @@ import theano
from theano.compat import PY3
from theano import gof
from theano.gof import (Op, utils, Variable, Constant, Type, Apply,
FunctionGraph)
FunctionGraph)
from theano.gof.python25 import partial, all, any
from theano.configparser import config
......@@ -137,7 +137,7 @@ class Scalar(Type):
py_type = self.dtype_specs()[0]
if strict and not isinstance(data, py_type):
raise TypeError("%s expected a %s, got %s of type %s" % (
self, py_type, data, type(data)), data)
self, py_type, data, type(data)), data)
try:
converted_data = py_type(data)
if (allow_downcast or
......@@ -148,10 +148,11 @@ class Scalar(Type):
return py_type(data)
else:
raise TypeError('Value cannot accurately be converted to dtype'
' (%s) and allow_downcast is not True' % self.dtype)
' (%s) and allow_downcast is not True' %
self.dtype)
except Exception, e:
raise TypeError("Could not convert %s (value=%s) to %s" % (
type(data), data, self.dtype), e)
type(data), data, self.dtype), e)
def values_eq_approx(self, a, b, tolerance=1e-4):
return abs(a - b) <= ((abs(a) + abs(b)) * tolerance)
......@@ -222,7 +223,7 @@ class Scalar(Type):
}[self.dtype]
except KeyError:
raise TypeError("Unsupported dtype for %s: %s" % (
self.__class__.__name__, self.dtype))
self.__class__.__name__, self.dtype))
def upcast(self, *others):
return upcast(*[x.dtype for x in [self] + list(others)])
......@@ -373,11 +374,11 @@ class Scalar(Type):
''' % dict(mytype=mytype, othertype=othertype)
operator_eq = ''.join(operator_eq_real(ctype, rtype)
for ctype in cplx_types
for rtype in real_types) \
for ctype in cplx_types
for rtype in real_types) \
+ ''.join(operator_eq_cplx(ctype1, ctype2)
for ctype1 in cplx_types
for ctype2 in cplx_types)
for ctype1 in cplx_types
for ctype2 in cplx_types)
# We are not using C++ generic templating here, because this would
# generate two different functions for adding a complex64 and a
......@@ -395,8 +396,8 @@ class Scalar(Type):
''' % dict(mytype=mytype, othertype=othertype)
operator_plus = ''.join(operator_plus_real(ctype, rtype)
for ctype in cplx_types
for rtype in real_types)
for ctype in cplx_types
for rtype in real_types)
def operator_minus_real(mytype, othertype):
return '''
......@@ -408,8 +409,8 @@ class Scalar(Type):
''' % dict(mytype=mytype, othertype=othertype)
operator_minus = ''.join(operator_minus_real(ctype, rtype)
for ctype in cplx_types
for rtype in real_types)
for ctype in cplx_types
for rtype in real_types)
def operator_mul_real(mytype, othertype):
return '''
......@@ -421,15 +422,15 @@ class Scalar(Type):
''' % dict(mytype=mytype, othertype=othertype)
operator_mul = ''.join(operator_mul_real(ctype, rtype)
for ctype in cplx_types
for rtype in real_types)
for ctype in cplx_types
for rtype in real_types)
return template % dict(nbits=64, half_nbits=32) \
+ template % dict(nbits=128, half_nbits=64) \
+ operator_eq \
+ operator_plus \
+ operator_minus \
+ operator_mul
+ template % dict(nbits=128, half_nbits=64) \
+ operator_eq \
+ operator_plus \
+ operator_minus \
+ operator_mul
else:
return ""
......@@ -448,11 +449,11 @@ class Scalar(Type):
# Register C code for ViewOp on Scalars.
theano.compile.register_view_op_c_code(
Scalar,
"""
%(oname)s = %(iname)s;
""",
1)
Scalar,
"""
%(oname)s = %(iname)s;
""",
1)
int8 = Scalar('int8')
......@@ -788,17 +789,18 @@ class ScalarOp(Op):
if output_types_preference is not None:
if not callable(output_types_preference):
raise TypeError(
"Expected a callable for the 'output_types_preference' argument to %s. (got: %s)" % (self.__class__, output_types_preference))
"Expected a callable for the 'output_types_preference' argument to %s. (got: %s)" %
self.__class__, output_types_preference)
self.output_types_preference = output_types_preference
def make_node(self, *inputs):
if self.nin >= 0:
if len(inputs) != self.nin:
raise TypeError("Wrong number of inputs for %s.make_node (got %i(%s), expected %i)" \
% (self, len(inputs), str(inputs), self.nin))
raise TypeError("Wrong number of inputs for %s.make_node (got %i(%s), expected %i)" %
self, len(inputs), str(inputs), self.nin)
inputs = [as_scalar(input) for input in inputs]
outputs = [t() for t in self.output_types([input.
type for input in inputs])]
outputs = [t() for t in self.output_types([input.type
for input in inputs])]
if len(outputs) != self.nout:
raise TypeError("Not the right number of outputs produced for %s(%s). Expected %s, got %s."
% (self, ", ".join(str(input) for input in inputs), self.nout, len(outputs)))
......@@ -906,6 +908,7 @@ class UnaryScalarOp(ScalarOp):
%(fct)s(n, x, z);
""" % locals()
class BinaryScalarOp(ScalarOp):
# One may define in subclasses the following fields:
# - `identity`: for an associative operation, identity corresponds to
......@@ -940,7 +943,7 @@ class FixedLogicalComparison(UnaryScalarOp):
return [int8]
def grad(self, inputs, output_gradients):
x ,= inputs
x, = inputs
out = self(x)
assert str(out.type.dtype).find('int') != -1
return [x.zeros_like().astype(theano.config.floatX)]
......@@ -1169,8 +1172,9 @@ class BinaryBitOp(BinaryScalarOp):
return upcast_out(*input_types[0])
def grad(self, inputs, output_gradients):
a,b = inputs
return [a.zeros_like().astype(theano.config.floatX), b.zeros_like().astype(theano.config.floatX)]
a, b = inputs
return [a.zeros_like().astype(theano.config.floatX),
b.zeros_like().astype(theano.config.floatX)]
class OR(BinaryBitOp):
......@@ -1237,7 +1241,7 @@ class Maximum(BinaryScalarOp):
raise NotImplementedError()
# Test for both y>x and x>=y to detect NaN
return ('%(z)s = ((%(y)s)>(%(x)s)? (%(y)s): '
'((%(x)s)>=(%(y)s)? (%(x)s): nan("")));' % locals())
'((%(x)s)>=(%(y)s)? (%(x)s): nan("")));' % locals())
def grad(self, (x, y), (gz, )):
assert gz.type not in complex_types
......@@ -1268,7 +1272,7 @@ class Minimum(BinaryScalarOp):
if any([i.type in complex_types for i in node.inputs]):
raise NotImplementedError()
return ('%(z)s = ((%(y)s)<(%(x)s)? (%(y)s): '
'((%(x)s)<=(%(y)s)? (%(x)s): nan("")));' % locals())
'((%(x)s)<=(%(y)s)? (%(x)s): nan("")));' % locals())
def grad(self, (x, y), (gz, )):
assert gz.type not in complex_types
......@@ -1307,7 +1311,7 @@ class Add(ScalarOp):
for ii, inp in enumerate(inputs):
if hasattr(inp, 'zeros_like'):
retval.append(
inp.zeros_like().astype(theano.config.floatX))
inp.zeros_like().astype(theano.config.floatX))
else:
retval.append(grad_undefined(self, ii, inp))
else:
......@@ -1342,9 +1346,10 @@ class Mul(ScalarOp):
output_type = self.output_types([i.type for i in inputs])[0]
if output_type in complex_types:
if not gz.type in complex_types:
raise TypeError('Mul with output_type ' + str(output_type) +\
' expected gz type to be complex, got gz with type ' +\
str(gz.type))
raise TypeError(
'Mul with output_type ' + str(output_type) +
' expected gz type to be complex, got gz with type ' +
str(gz.type))
if output_type in discrete_types:
return [ipt.zeros_like().astype(theano.config.floatX)
......@@ -1364,7 +1369,7 @@ class Mul(ScalarOp):
retval += [yr * real(gz) + yi * imag(gz)]
else:
retval += [mul(*([gz] + utils.difference(inputs,
[input])))]
[input])))]
return retval
......@@ -1420,10 +1425,10 @@ def int_or_true_div(x_discrete, y_discrete):
"`x.__truediv__(y)`.")
elif config.int_division == 'int':
warnings.warn(
"Division of two integer types with x / y is deprecated, "
"please use x // y for an integer division.",
DeprecationWarning,
stacklevel=4)
"Division of two integer types with x / y is deprecated, "
"please use x // y for an integer division.",
DeprecationWarning,
stacklevel=4)
return 'int'
elif config.int_division == 'floatX':
return 'true'
......@@ -1493,8 +1498,8 @@ true_div = TrueDiv(upcast_out, name='true_div')
class IntDiv(BinaryScalarOp):
complex_error = ComplexError(
"Theano does not support integer division (//) on "
"complex numbers, since numpy deprecated it.")
"Theano does not support integer division (//) on "
"complex numbers, since numpy deprecated it.")
def impl(self, x, y):
return x // y
......@@ -1575,8 +1580,8 @@ def mod_check(x, y):
class Mod(BinaryScalarOp):
complex_error = ComplexError(
"Theano does not support the mod operator (%) on "
"complex numbers, since numpy deprecated it.")
"Theano does not support the mod operator (%) on "
"complex numbers, since numpy deprecated it.")
def impl(self, x, y):
if isinstance(x, numpy.complex) or isinstance(y, numpy.complex):
......@@ -1874,7 +1879,7 @@ class Abs(UnaryScalarOp):
outputs = [float64()]
else:
outputs = [t() for t in self.output_types(
[input.type for input in inputs])]
[input.type for input in inputs])]
return Apply(self, inputs, outputs)
def impl(self, x):
......@@ -1989,7 +1994,7 @@ class RoundHalfToEven(UnaryScalarOp):
def c_code___(self, node, name, (x, ), (z, ), sub):
typ = node.outputs[0].type.dtype
if not node.outputs[0].type.dtype in ['float32', 'float64']:
if not typ in ['float32', 'float64']:
Exception("The output should be float32 or float64")
return dedent("""
......@@ -2036,7 +2041,7 @@ class RoundHalfToEven(UnaryScalarOp):
#undef ROUNDING_EPSILON
""")
""" % locals())
round_half_to_even = RoundHalfToEven(same_out_float_only)
......@@ -2750,15 +2755,14 @@ class Composite(ScalarOp):
subd[orphan] = orphan.type.c_literal(orphan.data)
else:
raise ValueError(
"All orphans in the fgraph to Composite must"
" be Constant instances.")
"All orphans in the fgraph to Composite must"
" be Constant instances.")
_c_code = "{\n"
i = 0
j = 0
self.nodenames = ["%(nodename)s_" + ('subnode%i' % j)
for j, n in enumerate(self.fgraph.toposort())]
for j, n in enumerate(self.fgraph.toposort())]
i = 0
for j, node in enumerate(self.fgraph.toposort()):
for output in node.outputs:
if output not in subd:
......@@ -2835,6 +2839,10 @@ class Composite(ScalarOp):
self.fgraph = fgraph
def __init__(self, inputs, outputs):
# We need to clone the graph as sometimes its nodes already
# contain a reference to an fgraph. As we want the Composite
# to be pickable, we can't have reference to fgraph.
inputs, outputs = gof.graph.clone(inputs, outputs)
self.inputs = copy(inputs)
self.outputs = copy(outputs)
self.inputs_type = tuple([input.type for input in inputs])
......
theano/scalar/tests/test_basic.py
浏览文件 @ 2c7949b6
......@@ -12,10 +12,14 @@ If you do want to rewrite these tests, bear in mind:
import unittest
import theano
from theano.gof import Variable, Op, FunctionGraph
from theano.gof import FunctionGraph
from theano import gof
from theano.scalar.basic import *
from theano.scalar.basic import (floats, float32, float64,
ints, int8, int32, complex64,
ComplexError, IntDiv, TrueDiv,
Composite, add, div_proxy,
and_, eq, neq, invert, mul)
def inputs():
......@@ -216,7 +220,7 @@ class test_div(unittest.TestCase):
d = float64()
f = float32()
print (a//b).owner.op
#print (a//b).owner.op
assert isinstance((a//b).owner.op, IntDiv)
assert isinstance((b//a).owner.op, IntDiv)
assert isinstance((b/d).owner.op, TrueDiv)
......
theano/tests/test_tutorial.py
浏览文件 @ 2c7949b6
......@@ -880,6 +880,56 @@ class T_using_gpu(unittest.TestCase):
for x in f.maker.fgraph.toposort()])
# Used in T_fibby
class Fibby(theano.Op):
"""
An arbitrarily generalized Fibbonacci sequence
"""
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, x):
x_ = theano.tensor.as_tensor_variable(x)
assert x_.ndim == 1
return theano.Apply(self,
inputs=[x_],
outputs=[x_.type()])
# using x_.type() is dangerous, it copies x's broadcasting
# behaviour
def perform(self, node, inputs, output_storage):
x, = inputs
y = output_storage[0][0] = x.copy()
for i in range(2, len(x)):
y[i] = y[i - 1] * y[i - 2] + x[i]
def c_code(self, node, name, inames, onames, sub):
x, = inames
y, = onames
fail = sub['fail']
return """
Py_XDECREF(%(y)s);
%(y)s = (PyArrayObject*)PyArray_FromArray(
%(x)s, 0, NPY_ARRAY_ENSURECOPY);
if (!%(y)s)
%(fail)s;
{//New scope needed to make compilation work
dtype_%(y)s * y = (dtype_%(y)s*)%(y)s->data;
dtype_%(x)s * x = (dtype_%(x)s*)%(x)s->data;
for (int i = 2; i < %(x)s->dimensions[0]; ++i)
y[i] = y[i-1]*y[i-2] + x[i];
}
""" % locals()
def c_code_cache_version(self):
return (1,)
class T_fibby(unittest.TestCase):
## All tests here belong to
## http://deeplearning.net/software/theano/extending/fibby.html
......@@ -888,54 +938,8 @@ class T_fibby(unittest.TestCase):
def test_fibby_1(self):
class Fibby(theano.Op):
"""
An arbitrarily generalized Fibbonacci sequence
"""
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, x):
x_ = theano.tensor.as_tensor_variable(x)
assert x_.ndim == 1
return theano.Apply(self,
inputs=[x_],
outputs=[x_.type()])
# using x_.type() is dangerous, it copies x's broadcasting
# behaviour
def perform(self, node, inputs, output_storage):
x, = inputs
y = output_storage[0][0] = x.copy()
for i in range(2, len(x)):
y[i] = y[i - 1] * y[i - 2] + x[i]
def c_code(self, node, name, inames, onames, sub):
x, = inames
y, = onames
fail = sub['fail']
return """
Py_XDECREF(%(y)s);
%(y)s = (PyArrayObject*)PyArray_FromArray(
%(x)s, 0, NPY_ARRAY_ENSURECOPY);
if (!%(y)s)
%(fail)s;
{//New scope needed to make compilation work
dtype_%(y)s * y = (dtype_%(y)s*)%(y)s->data;
dtype_%(x)s * x = (dtype_%(x)s*)%(x)s->data;
for (int i = 2; i < %(x)s->dimensions[0]; ++i)
y[i] = y[i-1]*y[i-2] + x[i];
}
""" % locals()
def c_code_cache_version(self):
return (1,)
# The definition of class Fibby is done outside of the test,
# so the object can be pickled.
fibby = Fibby()
from theano.tensor.opt import (get_scalar_constant_value,
......
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