merge

theano/gof/graph.py

@@ -417,7 +417,7 @@ def stack_search(start, expand, mode='bfs', build_inv = False):
         raise ValueError('mode should be bfs or dfs', mode)
     rval_set = set()
     rval_list = list()
-    if mode is 'bfs': start_pop = start.popleft
+    if mode == 'bfs': start_pop = start.popleft
     else: start_pop = start.pop
     expand_inv = {}
     while start:

theano/scan.py

@@ -106,7 +106,7 @@ def map( fn
 
     :param go_backwards: Boolean value that decides the direction of
                          iteration. True means that sequences are parsed
-                         from the end towards the begining, while False
+                         from the end towards the beginning, while False
                          is the other way around.
 
     :param mode: See ``scan``.
@@ -301,7 +301,7 @@ def scan( fn
             scan)
 
         The order of the sequences is the same as the one in the list
-        `sequences` given to scan. The order of the outputs is the sane
+        `sequences` given to scan. The order of the outputs is the same
         as the order of ``output_info``. For any sequence or output the
         order of the time slices is the same as the order of the time
         taps provided. For example if one writes the following :
@@ -314,7 +314,7 @@ def scan( fn
                    , outputs_info = [ dict( Output1, taps = [-3,-5])
                                     , dict( Output2, taps = None)
                                     , Output3 ]
-                   , non_sequences = [ Argument1, Argument 2])
+                   , non_sequences = [ Argument1, Argument2])
 
         ``fn`` should expect the following arguments in this given order:
 
@@ -341,7 +341,7 @@ def scan( fn
         `fn` should return an update dictionary ( that tells how to
         update any shared variable after each iteration ste). The
         dictionary can optionally be given as a list of tuples. There is
-        no constraint on the order of these two list, ``fn`` can return
+        no constraint on the order of these two lists, ``fn`` can return
         either ``(outputs_list, update_dictionary)`` or ``(update_dictionary,
         outputs_list)`` or just one of the two (in case the other is
         empty).
@@ -369,7 +369,7 @@ def scan( fn
     :param outputs_info:
         ``outputs_info`` is the list of Theano variables or dictionaries
         describing the initial state of the outputs computed
-        recurrently. When this initial states are given as dictionary
+        recurrently. When this initial state is given as a dictionary,
         optional information can be provided about the output corresponding
         to these initial states. The dictionary should have the following
         keys:
@@ -388,11 +388,11 @@ def scan( fn
           the initial state, which in this case should have the shape
           (5,)+output.shape. If this variable containing the initial
           state is called ``init_y`` then ``init_y[0]`` *corresponds to*
-          ``output[-5]``; ``init_y[1]`` *correponds to* ``output[-4]``;
+          ``output[-5]``; ``init_y[1]`` *corresponds to* ``output[-4]``;
           ``init_y[2]`` corresponds to ``output[-3]``; ``init_y[3]``
           coresponds to ``output[-2]``; ``init_y[4]`` corresponds to
           ``output[-1]``. While this order might seem strange, it comes
-          natural from splitting an array at a given point. Assume that
+          naturally from splitting an array at a given point. Assume that
           we have a array ``x``, and we choose ``k`` to be time step
           ``0``. Then our initial state would be ``x[:k]``, while the
           output will be ``x[k:]``. Looking at this split, elements in
@@ -401,17 +401,10 @@ def scan( fn
           ``fn``. They are provided as a list of *negative* integers,
           where a value ``k`` implies that at iteration step ``t`` scan will
           pass to ``fn`` the slice ``t+k``.
-        * ``inplace`` -- One of the Theano variables provided as
-          ``sequences``. ``scan`` will try to compute this output *in
-          place* of the provided input *iff* it respects the following
-          constraints:
-
-            * There is no other output that is denied to be computed in
-              place for whatever reason.
-
-            * ``fn`` is not using past taps of the input sequence that
-              will get overwritten by the output
-
+        * ``inplace`` -- DEPRECATED. Previously, one could specify with this
+          option whether the output should overwrite some particular input,
+          but it is now inferred automatically. If you specify this option
+          it will be ignored.
         * ``return_steps`` -- Integer representing the number of steps
           to return for the current steps. For example, if ``k`` is
           provided, ``scan`` will return ``output[-k:]``. This is meant as a
@@ -422,7 +415,7 @@ def scan( fn
         * ``store_steps`` -- Integer representing the number of
           intermediate steps ``scan`` should use for a given output. Use
           this key only if you really know what you are doing. In general
-          it is recommended to let scan decide for you the ammount of memory
+          it is recommended to let scan decide for you the amount of memory
           it should use.
 
         ``scan`` will follow this logic if partial information is given:
@@ -437,12 +430,12 @@ def scan( fn
         * If you wrap an output in a dictionary but you do not provide any
           initial state, it assumes that you are not using any form of
           taps.
-        * If you provide a ``None`` instead of a variable or a dictionary
+        * If you provide ``None`` instead of a variable or a dictionary
           ``scan`` assumes that you will not use any taps for this output
           (like for example in case of a map)
 
         If ``outputs_info`` is an empty list or None, ``scan`` assumes
-        that no tap is used for any of the otuputs. If information is
+        that no tap is used for any of the outputs. If information is
         provided just for a subset of the outputs an exception is
         raised (because there is no convention on how scan should map
         the provided information to the outputs of ``fn``)
@@ -450,8 +443,8 @@ def scan( fn
 
     :param non_sequences:
         ``non_sequences`` is the list of arguments that are passed to
-        ``fn`` at each steps. One can opt to exclude shared variables
-        used in ``fn`` from this list.
+        ``fn`` at each step. It is not necessary to list shared variables
+        used in ``fn`` here, since they will be identified automatically.
 
 
     :param n_steps:
@@ -469,9 +462,10 @@ def scan( fn
 
     :param truncate_gradient:
         ``truncate_gradient`` is the number of steps to use in truncated
-        BPTT.  If you compute gradients through a scan op, they are
+        BPTT (backpropagation through time).  If you compute gradients
+        through a scan op, they are
         computed using backpropagation through time. By providing a
-        different value then -1, you choose to use truncated BPTT instead
+        different value than -1, you choose to use truncated BPTT instead
         of classical BPTT, where you go for only ``truncate_gradient``
         number of steps back in time.
 
@@ -512,33 +506,32 @@ def scan( fn
     """
     # General observation : this code is executed only once, at creation
     # of the computational graph, so we don't yet need to be smart about
-    # anything ( to speed things up)
+    # anything (to speed things up)
 
     # check if inputs are just single variables instead of lists
-    if not (type(sequences) in (list, tuple)) and sequences != None:
-        seqs = [sequences]
-    elif sequences == None:
+    if sequences == None:
         seqs = []
+    elif not (type(sequences) in (list, tuple)):
+        seqs = [sequences]
     else:
         seqs = sequences
 
-    if not (type(outputs_info) in (list,tuple)) and outputs_info != None:
-        outs_info = [outputs_info]
-    elif outputs_info == None:
+    if outputs_info == None:
         outs_info = []
+    elif not (type(outputs_info) in (list,tuple)):
+        outs_info = [outputs_info]
     else:
         outs_info = outputs_info
 
-    if ( not (type(non_sequences) in (list,tuple))
-        and non_sequences != None):
-        non_seqs = [non_sequences]
-    elif non_sequences == None:
+    if non_sequences == None:
         non_seqs = []
+    elif not (type(non_sequences) in (list,tuple)):
+        non_seqs = [non_sequences]
     else:
         non_seqs = non_sequences
 
 
-    # If we provided a known number of steps ( before compilation)
+    # If we provided a known number of steps (before compilation)
     # and if that number is 1 or -1, then we can skip the Scan Op,
     # and just apply the inner function once
     # To do that we check here to see the nature of n_steps
@@ -570,7 +563,7 @@ def scan( fn
     sequences_taps = {}
     outputs_taps   = {}
 
-    # Assume that for any output we want to store everythin that it produces
+    # Assume that for any output we want to store everything that it produces
     store_steps = []
     return_steps = {}
 
@@ -591,8 +584,8 @@ def scan( fn
             # See if the user actually provided the None value to taps,
             # which would indicate that the sequence was provided but
             # not used by the internal function; Only if the user has
-            # not provided anything add the defaul [0]
-            # Possible reason to provide a squence and not use it  is
+            # not provided anything add the default [0]
+            # A possible reason to provide a sequence and not use it is
             # if you want to compute the output
             # inplace of this input; it is a very unlikely behaviour but
             # we do want to cover it for completeness
@@ -635,7 +628,7 @@ def scan( fn
                 raise ValueError('If you are using slices of an output you need to '\
                         'provide an initial state for it', outs_info[i])
                 # if there is an intial state but no tap, we will add the default value
-                # for taps, namely [-1] ( previous value); not that this will happen
+                # for taps, namely [-1] ( previous value); note that this will happen
                 # even though you have provided for taps the value None, which is a bit
                 # strange (why would one provide an initial state but tell scan not to
                 # use it ? ), just that in that case we will throw in a warning message
@@ -658,9 +651,14 @@ def scan( fn
 
         if outs_info[i].get('taps', None):
             # Create a separate outputs_taps dictionary with all the outputs taps; This
-            # is how the Scan Op expects this information, separeted from the variables
+            # is how the Scan Op expects this information, separated from the variables
             outputs_taps[i] = outs_info[i]['taps']
         if outs_info[i].get('inplace', None):
+
+            warning("DEPRECATED: you should not set the inplace parameter for an output in scan(...). "
+                    "This can cause problems for the early stages of the optimizer "
+                    "and there is a late optimization which automatically figures it out.")
+
             # The same is true for the inplace info; it has to go into a separate
             # dictionary based on index; Note that the input we're replacing should also
             # come as an index, therefore we have to look for it at this point

theano/tensor/basic.py

@@ -1336,35 +1336,39 @@ def _redefine_asRoutine(real_symbol_value):
         return real_symbol_value
     return decorator
 
-def _scal_elemwise(symbol):
+def _scal_elemwise_with_nfunc(nfunc, nin, nout):
     """Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
-    symbolname = symbol.__name__
-    inplace = symbolname.endswith('_inplace')
-    if inplace:
-        msg = "inplace"
-    else:
-        msg = "no_inplace"
-    n="Elemwise{%s,%s}"%(symbolname,msg)
+    def construct(symbol):
+        symbolname = symbol.__name__
+        inplace = symbolname.endswith('_inplace')
+        if inplace:
+            msg = "inplace"
+        else:
+            msg = "no_inplace"
+        n="Elemwise{%s,%s}"%(symbolname,msg)
 
-    if inplace:
-        scalar_op = getattr(scal, symbolname[:-len('_inplace')])
-        inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
-        rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=n)
-    else:
-        scalar_op = getattr(scal, symbolname)
-        rval = elemwise.Elemwise(scalar_op, name=n)
+        if inplace:
+            scalar_op = getattr(scal, symbolname[:-len('_inplace')])
+            inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
+            rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=n, nfunc_spec=((nfunc, nin, nout) if nfunc else None))
+        else:
+            scalar_op = getattr(scal, symbolname)
+            rval = elemwise.Elemwise(scalar_op, name=n, nfunc_spec=((nfunc, nin, nout) if nfunc else None))
 
-    if getattr(symbol, '__doc__', False):
-        rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
+        if getattr(symbol, '__doc__', False):
+            rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
 
-    #for the meaning of this see the ./epydoc script
-    # it makes epydoc display rval as if it were a function, not an object
-    rval.__epydoc_asRoutine = symbol
-    rval.__module__ = 'tensor'
+        #for the meaning of this see the ./epydoc script
+        # it makes epydoc display rval as if it were a function, not an object
+        rval.__epydoc_asRoutine = symbol
+        rval.__module__ = 'tensor'
 
-    pprint.assign(rval, printing.FunctionPrinter(symbolname))
+        pprint.assign(rval, printing.FunctionPrinter(symbolname))
 
-    return rval
+        return rval
+    return construct
+
+_scal_elemwise = _scal_elemwise_with_nfunc(None, None, None)
 
 
 #########################
@@ -1865,27 +1869,27 @@ def largest(*args):
 # Comparison
 ##########################
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('less', 2, 1)
 def lt(a, b):
     """a < b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('greater', 2, 1)
 def gt(a, b):
     """a > b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('less_equal', 2, 1)
 def le(a, b):
     """a <= b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('greater_equal', 2, 1)
 def ge(a, b):
     """a >= b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('equal', 2, 1)
 def eq(a, b):
     """a == b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('not_equal', 2, 1)
 def neq(a, b):
     """a != b"""
 
@@ -1903,19 +1907,19 @@ def switch(cond, ift, iff):
 # Bit-wise
 ##########################
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('bitwise_and', 2, 1)
 def and_(a,b):
     """bitwise a & b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('bitwise_or', 2, 1)
 def or_(a,b):
     """bitwise a | b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('bitwise_xor', 2, 1)
 def xor(a,b):
     """bitwise a ^ b"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('invert', 1, 1)
 def invert(a):
     """bitwise ~a"""
 
@@ -1923,7 +1927,7 @@ def invert(a):
 # Math
 ##########################
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('abs', 1, 1)
 def abs_(a):
     """|`a`|
 
@@ -1934,43 +1938,43 @@ def abs_(a):
 
 pprint.assign(abs_, printing.PatternPrinter(('|%(0)s|', -1000)))
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('exp', 1, 1)
 def exp(a):
     """e^`a`"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('negative', 1, 1)
 def neg(a):
     """-a"""
 
-@_scal_elemwise
+@_scal_elemwise # numpy.reciprocal does integer division on integer inputs (which is not very interesting)
 def inv(a):
     """1.0/a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('log', 1, 1)
 def log(a):
     """base e logarithm of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('log2', 1, 1)
 def log2(a):
     """base 2 logarithm of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('log10', 1, 1)
 def log10(a):
     """base 10 logarithm of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('log1p', 1, 1)
 def log1p(a):
     """log(1+a)"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('sign', 1, 1)
 def sgn(a):
     """sign of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('ceil', 1, 1)
 def ceil(a):
     """ceiling of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('floor', 1, 1)
 def floor(a):
     """floor of a"""
 
@@ -1989,7 +1993,10 @@ def round(a, mode="half_away_from_zero"):
     else:
         raise Exception("round mode %s is not implemented."%mode)
 
-@_scal_elemwise
+# def __round_half_to_even(a, dest):
+#     dest[:] = numpy.around(a)
+
+@_scal_elemwise_with_nfunc('around', 1, 0)
 def round_half_to_even(a):
     """round_half_to_even(a)"""
 
@@ -1997,35 +2004,35 @@ def round_half_to_even(a):
 def round_half_away_from_zero(a):
     """round_half_away_from_zero(a)"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('square', 1, 1)
 def sqr(a):
     """square of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('sqrt', 1, 1)
 def sqrt(a):
     """square root of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('cos', 1, 1)
 def cos(a):
     """cosine of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('sin', 1, 1)
 def sin(a):
     """sine of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('tan', 1, 1)
 def tan(a):
     """tangent of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('cosh', 1, 1)
 def cosh(a):
     """hyperbolic cosine of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('sinh', 1, 1)
 def sinh(a):
     """hyperbolic sine of a"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('tanh', 1, 1)
 def tanh(a):
     """hyperbolic tangent of a"""
 
@@ -2037,19 +2044,19 @@ def erf(a):
 def erfc(a):
     """complementary error function"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('real', 1, 0)
 def real(z):
     """Return real component of complex-valued tensor `z`"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('imag', 1, 0)
 def imag(z):
     """Return imaginary component of complex-valued tensor `z`"""
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('angle', 1, 0)
 def angle(z):
     """Return polar-coordinate angle of complex-valued tensor `z`"""
 
-@_scal_elemwise
+@_scal_elemwise # numpy.complex cannot build tensors
 def complex(real, imag):
     """Return complex-valued tensor with `real` and `imag` components"""
 
@@ -2475,13 +2482,13 @@ setdefault = default # legacy
 ##########################
 # Arithmetics
 ##########################
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('maximum', 2, 1)
 def maximum(x,y):
     """elemwise maximum. See max for the maximum in one tensor
     """
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('minimum', 2, 1)
 def minimum(x,y):
     """elemwise minimum. See min for the minimum in one tensor
     """
@@ -2495,47 +2502,47 @@ def div_proxy(x, y):
     else:
         return true_div(x, y)
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('add', 2, 1)
 def add(a, *other_terms):
     """elementwise addition"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('subtract', 2, 1)
 def sub(a, b):
     """elementwise subtraction"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('multiply', 2, 1)
 def mul(a, *other_terms):
     """elementwise multiplication"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('true_divide', 2, 1)
 def true_div(a, b):
     """elementwise [true] division (inverse of multiplication)"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('floor_divide', 2, 1)
 def floor_div(a, b):
     """elementwise [floor] division (inverse of multiplication)"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('floor_divide', 2, 1) # not a c/p error, floor_div and int_div are the same thing
 def int_div(a, b):
     """elementwise integer-division"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('mod', 2, 1)
 def mod(a, b):
     """elementwise modulo"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('power', 2, 1)
 def pow(a, b):
     """elementwise power"""
     # see decorator for function body
 
-@_scal_elemwise
+@_scal_elemwise_with_nfunc('clip', 3, 1)
 def clip(x, min, max):
     """clip x to be between min and max"""
     # see decorator for function body

theano/tensor/elemwise.py

@@ -361,6 +361,21 @@ class DimShufflePrinter:
 pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, DimShuffle), DimShufflePrinter())
 
 
+def _make_nfunc(name, nin, nout):
+    f = getattr(numpy, name)
+    return f
+
+    # if name.endswith("*"):
+    #     name = name[:-1]
+    #     f = getattr(numpy, name)
+    #     def fn(*args):
+    #         args[-1][:] = f(*(args[:-1]))
+    #     return fn
+    # else:
+    #     f = getattr(numpy, name)
+    #     return f
+
+
 ################
 ### Elemwise ###
 ################
@@ -392,7 +407,7 @@ class Elemwise(Op):
       Elemwise(log)(rand(3, 4, 5))
     """
 
-    def __init__(self, scalar_op, inplace_pattern = {}, name = None):
+    def __init__(self, scalar_op, inplace_pattern = {}, name = None, nfunc_spec = None):
         """
         Usage: Elemwise(scalar_op, inplace_pattern = {})
 
@@ -406,10 +421,14 @@ class Elemwise(Op):
         self.scalar_op = scalar_op
         self.inplace_pattern = inplace_pattern
         self.destroy_map = dict((o, [i]) for o, i in inplace_pattern.items())
-        if scalar_op.nin > 0:
+
+        self.ufunc = None
+        self.nfunc = None
+        self.nfunc_spec = nfunc_spec
+        if nfunc_spec:
+            self.nfunc = _make_nfunc(*nfunc_spec)
+        elif scalar_op.nin > 0:
             self.ufunc = numpy.frompyfunc(scalar_op.impl, scalar_op.nin, scalar_op.nout)
-        else:
-            self.ufunc = None
 
         #precompute the hash of this node
         self._rehash()
@@ -417,16 +436,19 @@ class Elemwise(Op):
     def __getstate__(self):
         d = copy(self.__dict__)
         d.pop('ufunc')
+        d.pop('nfunc')
         d.pop('__epydoc_asRoutine', None)
         d.pop('_hashval')
         return d
 
     def __setstate__(self, d):
         self.__dict__.update(d)
-        if self.scalar_op.nin > 0:
+        self.ufunc = None
+        self.nfunc = None
+        if getattr(self, 'nfunc_spec', None):
+            self.nfunc = _make_nfunc(*self.nfunc_spec)
+        elif self.scalar_op.nin > 0:
             self.ufunc = numpy.frompyfunc(self.scalar_op.impl, self.scalar_op.nin, self.scalar_op.nout)
-        else:
-            self.ufunc = None
         self._rehash()
 
     def make_node(self, *inputs):
@@ -621,10 +643,16 @@ class Elemwise(Op):
                     else:
                         odat = numpy.ndarray(shape, dtype = output.type.dtype)
                 storage[0] = odat
-        # the second calling form is used because in certain versions of numpy
-        # the first (faster) version leads to segfaults
+
         ufunc_args = inputs # + output_storage
-        ufunc = self.ufunc or numpy.frompyfunc(self.scalar_op.impl, len(inputs), self.scalar_op.nout)
+        if self.nfunc and len(inputs) == self.nfunc_spec[1]:
+            ufunc = self.nfunc
+            nout = 1
+        else:
+            # the second calling form is used because in certain versions of numpy
+            # the first (faster) version leads to segfaults
+            ufunc = self.ufunc or numpy.frompyfunc(self.scalar_op.impl, len(inputs), self.scalar_op.nout)
+            nout = ufunc.nout
 
         try:
             variables = ufunc(*ufunc_args)
@@ -633,7 +661,7 @@ class Elemwise(Op):
                         'for params of shape', [arg.shape for arg in ufunc_args]
             e.args = e.args + errormsg
             raise
-        if ufunc.nout == 1: variables = [variables]
+        if nout == 1: variables = [variables]
         for variable, storage in zip(variables, output_storage):
             if hasattr(variable,'shape') and storage[0].shape != variable.shape:
                 storage[0].resize(variable.shape)