Remove apply_shape, and tag.shape of variables. Closes #633.

theano/gof/apply_shape.py

-"""Apply for use with Tensors that implements shape propagation via variable.tag.shape
-
-This is not used currently very used. It appear in some case, but I'm not sure it if work or if it is used by default.
-It could help the current system to make it detect problem earlier when contructing the graph instead of during optimization.
-"""
-import sys
-from theano import gof
-
-def ishape(v):
-    try:
-        return (True, v.tag.shape)
-    except AttributeError:
-        return (False, (None,)*v.type.ndim)
-
-
-class Apply(gof.Apply):
-    def __init__(self, op, inputs, outputs):
-        super(Apply, self).__init__(op, inputs, outputs)
-        if not inputs:
-            return
-        # if any input has any shape info, then propagate it
-        try:
-            provided, ishapes = zip(*[ishape(i) for i in inputs])
-        except AttributeError:
-            # i.type.ndim didn't make sense for some i
-            return
-        if provided == [False for i in inputs]:
-            # no input had a tag.shape
-            return
-        try:
-            infer_shape = op.infer_shape
-        except AttributeError:
-            # op has no infer_shape, that's fine
-            return
-
-        try:
-            oshapes = infer_shape(self, ishapes)
-        except NotImplementedError:
-            return
-
-        for o, oshp in zip(outputs, oshapes):
-            o.tag.shape = oshp

theano/tensor/basic.py

@@ -12,8 +12,7 @@ import numpy, theano
 #from copy import copy as python_copy
 
 from theano import gof, shared
-from theano.gof import Variable, Op, Type, Constant,  Value
-from theano.gof.apply_shape import Apply
+from theano.gof import Apply, Constant, Op, Type, Value, Variable
 
 from theano import gradient
 
@@ -287,7 +286,6 @@ def constant_or_value(x, rtype, name=None, ndim=None, dtype=None):
                     TensorType(dtype = x_.dtype, broadcastable = bcastable),
                     x_.copy(),
                     name=name)
-            rval.tag.shape = x_.shape
             return rval
         else:
             # leave the shape out of the type
@@ -3501,25 +3499,12 @@ class Join(Op):
 
 
     def infer_shape(self, node, ishapes):
-        # Join op should get at least two inputs to join
+        # ishapes[0] contains the size of the axis on which we join
+        # Join op should get at least one input to join
         assert len(ishapes) > 1
-        # Not sure this is needed anymore :( ... basically the apply_shape
-        # version of the apply node (i.e. the one defined in
-        # gof/apply_shape) calls infer_shape methods passing None to unknown
-        # inputs. It can handle NotImplementedError, so for now I just raise
-        # that whenever I get a None. Should we just remove gof/apply_shape
-        # if it is depricated ??
-        if ishapes[1] is None:
-            raise NotImplementedError
         n_dim = len(ishapes[1])
         for shape in ishapes[1:]:
-            if shape is None:
-                raise NotImplementedError
-            for shape_i in shape:
-                if shape_i is None:
-                    raise NotImplementedError
-            # at this point the inputs have been broadcasted so they should
-            # all have the same shape
+            assert shape is not None
             assert len(shape) == n_dim
 
         out_shapes = []
@@ -3837,9 +3822,6 @@ def reshape(x, newshape, ndim=None, name=None):
         ndim = get_vector_length(newshape)
     op = Reshape(ndim, name)
     rval = op(x, newshape)
-
-    if  isinstance(newshape, (list, tuple)):
-        rval.tag.shape = newshape
     return rval
 
 class Flatten(Op):

theano/tensor/blas.py

@@ -6,16 +6,13 @@ import numpy.distutils
 from theano.configparser import config, AddConfigVar, StrParam
 from theano.gof import (utils, Op, view_roots, PatternSub, DestroyHandler,
         SeqOptimizer, local_optimizer, Optimizer, LocalOptimizer, OpKeyOptimizer,
-        InconsistencyError, toolbox, SequenceDB, EquilibriumOptimizer)
+        InconsistencyError, toolbox, SequenceDB, EquilibriumOptimizer, Apply)
 from theano.printing import pprint, FunctionPrinter, debugprint
 from theano.compile.mode import optdb
 from theano.gof.python25 import all, any
 import theano.scalar
 import basic as T
 
-
-from theano.gof.apply_shape import Apply
-
 #NB: this clobbers the builtin 'compile' symbol
 from theano import compile  #to register the optimizer built by this file
 

theano/tensor/elemwise.py

@@ -5,12 +5,11 @@ import numpy
 import elemwise_cgen as cgen
 import theano
 from theano import gof
-from theano.gof import Op
+from theano.gof import Apply, Op
 from theano import scalar
 from theano.scalar import Scalar
 from theano.printing import pprint
 from theano.gof.python25 import all, any
-from theano.gof.apply_shape import Apply
 
 
 # tensor depends on elemwise to provide definitions for several ops

theano/tensor/nnet/conv.py

@@ -18,7 +18,7 @@ import theano
 from theano.tensor import (as_tensor_variable, blas, get_constant_value,
         patternbroadcast)
 from theano import Op, config
-from theano.gof.apply_shape import Apply
+from theano.gof import Apply
 from theano.gof.python25 import any
 
 imported_scipy_signal = False

theano/tensor/nnet/nnet.py

@@ -11,7 +11,7 @@ from theano.tensor import basic as tensor
 from theano.tensor import elemwise, dmatrix, fmatrix, dvector, fvector
 from theano.tensor import opt
 from theano.compile import optdb
-from theano.gof.apply_shape import Apply
+from theano.gof import Apply
 
 from theano.tensor.nnet.sigm import sigmoid, softplus