Merge pull request #1482 from nouiz/rnade

doc/library/config.txt

@@ -482,6 +482,14 @@ import theano and print the config variable, as in:
 
     This flag's value cannot be modified during the program execution.
 
+.. attribute:: optimizer_verbose
+
+    Bool value: either True or False
+
+    Default: False
+
+    When True, we print on the stdout the optimization applied.
+
 .. attribute:: nocleanup
 
     Bool value: either True or False
@@ -630,6 +638,12 @@ import theano and print the config variable, as in:
           this Op
         - ``'raise'`` will raise an Exception
     
+.. attribute:: config.compute_test_value_opt
+
+   As ``compute_test_value``, but it is the value used during Theano
+   optimization phase. Theano user's do not need to use this. This is
+   to help debug shape error in Theano optimization.
+
 .. attribute:: config.exception_verbosity
 
     String Value: ``'low'``, ``'high'``.

theano/compile/debugmode.py

@@ -1428,21 +1428,25 @@ class _VariableEquivalenceTracker(object):
         self.reasons = {}
         self.replaced_by = {}
         self.event_list = []
+        for node in fgraph.toposort():
+            self.on_import(fgraph, node, "on_attach")
 
     def on_detach(self, fgraph):
         assert fgraph is self.fgraph
         self.fgraph = None
 
-    def on_prune(self, fgraph, node):
-        self.event_list.append(_FunctionGraphEvent('prune', node))
+    def on_prune(self, fgraph, node, reason):
+        self.event_list.append(_FunctionGraphEvent('prune', node,
+                                                   reason=reason))
         #print 'PRUNING NODE', node, id(node)
         assert node in self.active_nodes
         assert node not in self.inactive_nodes
         self.active_nodes.remove(node)
         self.inactive_nodes.add(node)
 
-    def on_import(self, fgraph, node):
-        self.event_list.append(_FunctionGraphEvent('import', node))
+    def on_import(self, fgraph, node, reason):
+        self.event_list.append(_FunctionGraphEvent('import', node,
+                                                   reason=reason))
 
         #print 'NEW NODE', node, id(node)
         assert node not in self.active_nodes
@@ -2114,7 +2118,7 @@ class _Maker(FunctionMaker):  # inheritance buys a few helper functions
             # optimize the fgraph
             compute_test_value_orig = theano.config.compute_test_value
             try:
-                theano.config.compute_test_value = "off"
+                theano.config.compute_test_value = theano.config.compute_test_value_opt
                 optimizer(fgraph)
 
                 theano.compile.function_module.insert_deepcopy(fgraph, inputs,

theano/compile/function_module.py

@@ -1018,7 +1018,7 @@ class FunctionMaker(object):
         compute_test_value_orig = theano.config.compute_test_value
         add_stack_trace_on_call = gof.Op.add_stack_trace_on_call
         try:
-            theano.config.compute_test_value = "off"
+            theano.config.compute_test_value = theano.config.compute_test_value_opt
             gof.Op.add_stack_trace_on_call = False
             start_optimizer = time.time()
             optimizer_profile = optimizer(fgraph)

theano/configdefaults.py

@@ -157,6 +157,11 @@ AddConfigVar('optimizer',
         EnumStr('fast_run', 'merge', 'fast_compile', 'None'),
         in_c_key=False)
 
+AddConfigVar('optimizer_verbose',
+             "If True, we print all optimization being applied",
+             BoolParam(False),
+             in_c_key=False)
+
 AddConfigVar('on_opt_error',
         ("What to do when an optimization crashes: warn and skip it, raise "
          "the exception, or fall into the pdb debugger."),
@@ -379,10 +384,17 @@ AddConfigVar('compute_test_value',
          "Constants, SharedVariables and the tag 'test_value' as inputs "
          "to the function. This helps the user track down problems in the "
          "graph before it gets optimized."),
-        EnumStr('off', 'ignore', 'warn', 'raise'),
+        EnumStr('off', 'ignore', 'warn', 'raise', 'pdb'),
         in_c_key=False)
 
 
+AddConfigVar('compute_test_value_opt',
+             ("For debugging Theano optimization only."
+              " Same as compute_test_value, but is used"
+              " during Theano optimization"),
+             EnumStr('off', 'ignore', 'warn', 'raise', 'pdb'),
+             in_c_key=False)
+
 """Note to developers:
     Generally your exceptions should use an apply node's __str__
     method when exception_verbosity == 'low'. When exception_verbosity

theano/gof/destroyhandler.py

@@ -380,7 +380,7 @@ if 0:
             delattr(self.fgraph, 'destroy_handler')
             self.fgraph = None
 
-        def on_import(self, fgraph, app):
+        def on_import(self, fgraph, app, reason):
             """Add Apply instance to set which must be computed"""
 
             #if app in self.debug_all_apps: raise ProtocolError("double import")
@@ -410,7 +410,7 @@ if 0:
 
             self.stale_droot = True
 
-        def on_prune(self, fgraph, app):
+        def on_prune(self, fgraph, app, reason):
             """Remove Apply instance from set which must be computed"""
             #if app not in self.debug_all_apps: raise ProtocolError("prune without import")
             #self.debug_all_apps.remove(app)
@@ -765,7 +765,7 @@ class DestroyHandler(toolbox.Bookkeeper):
         delattr(self.fgraph, 'destroy_handler')
         self.fgraph = None
 
-    def on_import(self, fgraph, app):
+    def on_import(self, fgraph, app, reason):
         """Add Apply instance to set which must be computed"""
 
         if app in self.debug_all_apps: raise ProtocolError("double import")
@@ -795,7 +795,7 @@ class DestroyHandler(toolbox.Bookkeeper):
 
         self.stale_droot = True
 
-    def on_prune(self, fgraph, app):
+    def on_prune(self, fgraph, app, reason):
         """Remove Apply instance from set which must be computed"""
         if app not in self.debug_all_apps: raise ProtocolError("prune without import")
         self.debug_all_apps.remove(app)

theano/gof/fg.py

@@ -5,6 +5,7 @@ Contains the FunctionGraph class and exception
 types that it can raise
 """
 import sys
+
 from theano.gof import graph
 from theano.gof import utils
 from theano.gof import toolbox
@@ -16,6 +17,7 @@ NullType = None
 from theano.gof.python25 import OrderedDict
 from theano.misc.ordered_set import OrderedSet
 
+
 class InconsistencyError(Exception):
     """
     This exception should be thrown by listeners to FunctionGraph when the
@@ -82,7 +84,8 @@ class FunctionGraph(utils.object2):
         # so I probably am) this should be a set.
         self._features = []
 
-        # All apply nodes in the subgraph defined by inputs and outputs are cached in this field
+        # All apply nodes in the subgraph defined by inputs and
+        # outputs are cached in this field
         self.apply_nodes = set()
 
         # Ditto for variable nodes
@@ -104,7 +107,7 @@ class FunctionGraph(utils.object2):
             self.__setup_r__(input)
             self.variables.add(input)
 
-        self.__import_r__(outputs)
+        self.__import_r__(outputs, reason="init")
         for i, output in enumerate(outputs):
             output.clients.append(('output', i))
 
@@ -112,12 +115,12 @@ class FunctionGraph(utils.object2):
         self.variable_locks = {}
         self.profile = None
 
-
     ### Setup a Variable ###
-
     def __setup_r__(self, r):
         # sets up r so it belongs to this fgraph
-        if hasattr(r, 'fgraph') and r.fgraph is not None and r.fgraph is not self:
+        if (hasattr(r, 'fgraph') and
+            r.fgraph is not None and
+            r.fgraph is not self):
             raise Exception("%s is already owned by another fgraph" % r)
         r.fgraph = self
         r.clients = []
@@ -165,13 +168,13 @@ class FunctionGraph(utils.object2):
         self.inputs = None
         self.outputs = None
 
-
     ### clients ###
-
     def clients(self, r):
         """
         Set of all the (node, i) pairs such that node.inputs[i] is r.
-        Tell differently, a list of (node,i) such that each node have r as input at index i.
+        Tell differently, a list of (node,i) such that each node have
+        r as input at index i.
+
         """
         return r.clients
 
@@ -184,12 +187,15 @@ class FunctionGraph(utils.object2):
         """
         if set(r.clients).intersection(set(new_clients)):
             print >> sys.stderr, 'ERROR: clients intersect!'
-            print >> sys.stderr, '  RCLIENTS of', r, [(n,i, type(n), id(n)) for n,i in r.clients]
-            print >> sys.stderr, '  NCLIENTS of', r, [(n,i, type(n), id(n)) for n,i in new_clients]
+            print >> sys.stderr, '  RCLIENTS of', r, [(n, i, type(n), id(n))
+                                                      for n, i in r.clients]
+            print >> sys.stderr, '  NCLIENTS of', r, [(n, i, type(n), id(n))
+                                                      for n, i in new_clients]
         assert not set(r.clients).intersection(set(new_clients))
         r.clients += new_clients
 
-    def __remove_clients__(self, r, clients_to_remove, prune = True):
+    def __remove_clients__(self, r, clients_to_remove,
+                           prune=True, reason=None):
         """ WRITEME
         r -> variable
         clients_to_remove -> list of (op, i) pairs such that node.inputs[i] is not r anymore.
@@ -205,15 +211,13 @@ class FunctionGraph(utils.object2):
             assert entry not in r.clients  # an op,i pair should be unique
         if not r.clients:
             if prune:
-                self.__prune_r__([r])
+                self.__prune_r__([r], reason)
                 return False
             return True
         return False
 
-
     ### import ###
-
-    def __import_r__(self, variables):
+    def __import_r__(self, variables, reason):
         global NullType
         if NullType is None:
             from null_type import NullType
@@ -222,17 +226,18 @@ class FunctionGraph(utils.object2):
         for apply_node in [r.owner for r in variables if r.owner is not None]:
             if apply_node not in r_owner_done:
                 r_owner_done.add(apply_node)
-                self.__import__(apply_node)
+                self.__import__(apply_node, reason=reason)
         for r in variables:
             if r.owner is None and not isinstance(r, graph.Constant) and r not in self.inputs:
-                if isinstance(r.type,NullType):
-                    raise TypeError("Computation graph contains a NaN. "+r.type.why_null)
+                if isinstance(r.type, NullType):
+                    raise TypeError("Computation graph contains a NaN. " +
+                                    r.type.why_null)
                 raise MissingInputError("Undeclared input", r)
             if not getattr(r, 'fgraph', None) is self:
                 self.__setup_r__(r)
             self.variables.add(r)
 
-    def __import__(self, apply_node, check = True):
+    def __import__(self, apply_node, check=True, reason=None):
         node = apply_node
 
         # We import the nodes in topological order. We only are interested
@@ -248,7 +253,9 @@ class FunctionGraph(utils.object2):
                 for r in node.inputs:
                     if hasattr(r, 'fgraph') and r.fgraph is not self:
                         raise Exception("%s is already owned by another fgraph" % r)
-                    if r.owner is None and not isinstance(r, graph.Constant) and r not in self.inputs:
+                    if (r.owner is None and
+                        not isinstance(r, graph.Constant) and
+                        r not in self.inputs):
 
                         #Verbose error message
                         #Show a complete chain of variables from the missing input to an output
@@ -328,20 +335,18 @@ class FunctionGraph(utils.object2):
                     self.variables.add(input)
                 self.__add_clients__(input, [(node, i)])
             assert node.fgraph is self
-            self.execute_callbacks('on_import', node)
-
+            self.execute_callbacks('on_import', node, reason)
 
     ### prune ###
-
-    def __prune_r__(self, variables):
+    def __prune_r__(self, variables, reason=None):
         # Prunes the owners of the variables.
         for node in set(r.owner for r in variables if r.owner is not None):
-            self.__prune__(node)
+            self.__prune__(node, reason)
         for r in variables:
             if not r.clients and r in self.variables:
                 self.variables.remove(r)
 
-    def __prune__(self, apply_node):
+    def __prune__(self, apply_node, reason=None):
         node = apply_node
         if node not in self.apply_nodes:
             raise Exception("%s does not belong to this FunctionGraph and cannot be pruned." % node)
@@ -356,16 +361,13 @@ class FunctionGraph(utils.object2):
                 return
         self.apply_nodes.remove(node)
         self.variables.difference_update(node.outputs)
-        self.execute_callbacks('on_prune', node)
+        self.execute_callbacks('on_prune', node, reason)
 
         for i, input in enumerate(node.inputs):
-            self.__remove_clients__(input, [(node, i)])
+            self.__remove_clients__(input, [(node, i)], reason=reason)
         #self.__prune_r__(node.inputs)
 
-
-
     ### change input ###
-
     def change_input(self, node, i, new_r, reason=None):
         """WRITEME
         Changes node.inputs[i] to new_r.
@@ -398,25 +400,28 @@ class FunctionGraph(utils.object2):
         if r is new_r:
             return
 
-        self.__import_r__([new_r])
+        self.__import_r__([new_r], reason=reason)
         self.__add_clients__(new_r, [(node, i)])
         prune = self.__remove_clients__(r, [(node, i)], False)
         # Precondition: the substitution is semantically valid
         # However it may introduce cycles to the graph,  in which case the
         # transaction will be reverted later.
-        self.execute_callbacks('on_change_input', node, i, r, new_r, reason=reason)
+        self.execute_callbacks('on_change_input', node, i,
+                               r, new_r, reason=reason)
 
         if prune:
-            self.__prune_r__([r])
-
+            self.__prune_r__([r], reason=reason)
 
     ### replace ###
-
-    def replace(self, r, new_r, reason=None):
+    def replace(self, r, new_r, reason=None, verbose=None):
         """ WRITEME
         This is the main interface to manipulate the subgraph in FunctionGraph.
         For every node that uses r as input, makes it use new_r instead.
         """
+        if verbose is None:
+            verbose = config.optimizer_verbose
+        if verbose:
+            print reason, r, new_r
         if r.fgraph is not self:
             raise Exception("Cannot replace %s because it does not belong to this FunctionGraph" % r, str(reason))
         if not r.type == new_r.type:
@@ -440,8 +445,6 @@ class FunctionGraph(utils.object2):
         for r, new_r in pairs:
             self.replace(r, new_r, reason=reason)
 
-
-
     def extend(self, feature):
         warnings.warn("FunctionGraph.extend is deprecatd. It has been "
                       "renamed to FunctionGraph.attach_feature")
@@ -481,7 +484,9 @@ class FunctionGraph(utils.object2):
         """WRITEME
         Removes the feature from the graph.
 
-        Calls feature.on_detach(function_graph) if an on_detach method is defined.
+        Calls feature.on_detach(function_graph) if an on_detach method
+        is defined.
+
         """
         try:
             self._features.remove(feature)
@@ -491,9 +496,7 @@ class FunctionGraph(utils.object2):
         if detach is not None:
             detach(self)
 
-
     ### callback utils ###
-
     def execute_callbacks(self, name, *args, **kwargs):
         """WRITEME
         Calls
@@ -518,7 +521,6 @@ class FunctionGraph(utils.object2):
                 else:
                     raise
 
-
     def collect_callbacks(self, name, *args):
         """WRITEME
         Returns a dictionary d such that:
@@ -534,9 +536,7 @@ class FunctionGraph(utils.object2):
             d[feature] = fn(*args)
         return d
 
-
     ### misc ###
-
     def toposort(self):
         """WRITEME
         Returns an ordering of the graph's Apply nodes such that:
@@ -552,8 +552,8 @@ class FunctionGraph(utils.object2):
         if len(self.apply_nodes) < 2:
             # optimization
             # when there are 0 or 1 nodes, no sorting is necessary
-            # This special case happens a lot because the OpWiseCLinker produces
-            # 1-element graphs.
+            # This special case happens a lot because the OpWiseCLinker
+            # produces 1-element graphs.
             return list(self.apply_nodes)
         fg = self
 
@@ -568,8 +568,9 @@ class FunctionGraph(utils.object2):
         Return dict d s.t. d[node] is a list of nodes that must be evaluated
         before node itself can be evaluated.
 
-        This is used primarily by the destroy_handler feature to ensure that all
-        clients of any destroyed inputs have already computed their outputs.
+        This is used primarily by the destroy_handler feature to ensure that
+        all clients of any destroyed inputs have already computed their
+        outputs.
 
         :note: This only calls the orderings() fct on all features. It does not
                take care of computing dependencies by itself.
@@ -581,17 +582,19 @@ class FunctionGraph(utils.object2):
             if hasattr(feature, 'orderings'):
                 orderings = feature.orderings(self)
                 if not isinstance(orderings, OrderedDict):
-                    raise TypeError("Non-deterministic return value from " \
-                            +str(feature.orderings) \
-                            +". Nondeterministic object is "+str(orderings))
+                    raise TypeError("Non-deterministic return value from " +
+                                    str(feature.orderings) +
+                                    ". Nondeterministic object is " +
+                                    str(orderings))
                 for node, prereqs in orderings.items():
                     if not isinstance(prereqs, (list, OrderedSet)):
-                        raise TypeError("prereqs must be a type with a "
+                        raise TypeError(
+                            "prereqs must be a type with a "
                             "deterministic iteration order, or toposort "
                             " will be non-deterministic.")
                     ords.setdefault(node, []).extend(prereqs)
         # eliminate duplicate prereqs
-        for (node,prereqs) in ords.items():
+        for (node, prereqs) in ords.items():
             ords[node] = list(OrderedSet(prereqs))
         return ords
 
@@ -624,34 +627,48 @@ class FunctionGraph(utils.object2):
         if self.apply_nodes != nodes:
             missing = nodes.difference(self.apply_nodes)
             excess = self.apply_nodes.difference(nodes)
-            raise Exception("The nodes are inappropriately cached. missing, in excess: ", missing, excess)
+            raise Exception(
+                "The nodes are inappropriately cached. missing, in excess: ",
+                missing, excess)
         for node in nodes:
             if node.fgraph is not self:
-                raise Exception("Node should belong to the FunctionGraph.", node)
+                raise Exception("Node should belong to the FunctionGraph.",
+                                node)
             for i, variable in enumerate(node.inputs):
                 if variable.fgraph is not self:
-                    raise Exception("Input of node should belong to the FunctionGraph.", variable, (node, i))
+                    raise Exception(
+                        "Input of node should belong to the FunctionGraph.",
+                        variable, (node, i))
                 if (node, i) not in variable.clients:
-                    raise Exception("Inconsistent clients list.", (node, i), variable.clients)
+                    raise Exception("Inconsistent clients list.",
+                                    (node, i), variable.clients)
         variables = set(graph.variables(self.inputs, self.outputs))
         if set(self.variables) != variables:
             missing = variables.difference(self.variables)
             excess = self.variables.difference(variables)
-            raise Exception("The variables are inappropriately cached. missing, in excess: ", missing, excess)
+            raise Exception(
+                "The variables are inappropriately cached. missing, in excess: ",
+                missing, excess)
         for variable in variables:
-            if variable.owner is None and variable not in self.inputs and not isinstance(variable, graph.Constant):
+            if (variable.owner is None and
+                variable not in self.inputs and
+                not isinstance(variable, graph.Constant)):
                 raise Exception("Undeclared input.", variable)
             if variable.fgraph is not self:
-                raise Exception("Variable should belong to the FunctionGraph.", variable)
+                raise Exception("Variable should belong to the FunctionGraph.",
+                                variable)
             for node, i in variable.clients:
                 if node == 'output':
                     if self.outputs[i] is not variable:
-                        raise Exception("Inconsistent clients list.", variable, self.outputs[i])
+                        raise Exception("Inconsistent clients list.",
+                                        variable, self.outputs[i])
                     continue
                 if node not in nodes:
-                    raise Exception("Client not in FunctionGraph.", variable, (node, i))
+                    raise Exception("Client not in FunctionGraph.",
+                                    variable, (node, i))
                 if node.inputs[i] is not variable:
-                    raise Exception("Inconsistent clients list.", variable, node.inputs[i])
+                    raise Exception("Inconsistent clients list.",
+                                    variable, node.inputs[i])
 
     def __str__(self):
         return "[%s]" % ", ".join(graph.as_string(self.inputs, self.outputs))
@@ -659,9 +676,7 @@ class FunctionGraph(utils.object2):
     def __repr__(self):
         return self.__str__()
 
-
     ### clone ###
-
     def clone(self):
         """WRITEME"""
         return self.clone_get_equiv()[0]

theano/gof/op.py

@@ -13,6 +13,7 @@ __contact__   = "theano-dev <theano-dev@googlegroups.com>"
 __docformat__ = "restructuredtext en"
 
 import logging
+import sys
 import warnings
 
 import theano
@@ -408,6 +409,9 @@ class PureOp(object):
                     elif config.compute_test_value == 'ignore':
                         # silently skip test
                         run_perform = False
+                    elif config.compute_test_value == 'pdb':
+                        import pdb
+                        pdb.post_mortem(sys.exc_info()[2])
                     else:
                         raise ValueError('%s is invalid for option config.compute_Test_value' % config.compute_test_value)
 
@@ -638,8 +642,11 @@ def get_test_value(v):
     For a Shared variable, it is the internal value.
     For another Variable, it is the content of v.tag.test_value.
     """
-    v_tensor = theano.tensor.as_tensor_variable(v)
-    return PureOp._get_test_value(v_tensor)
+    if not isinstance(v, graph.Variable):
+        v_var = theano.tensor.as_tensor_variable(v)
+    else:
+        v_var = v
+    return PureOp._get_test_value(v_var)
 
 
 def missing_test_message(msg):

theano/gof/opt.py

@@ -421,7 +421,7 @@ class MergeFeature(object):
         self.blacklist = []
 
         for node in fgraph.toposort():
-            self.on_import(fgraph, node)
+            self.on_import(fgraph, node, "on_attach")
 
     def on_change_input(self, fgraph, node, i, r, new_r):
         # If inputs to node change, it is not guaranteed that it is distinct
@@ -433,14 +433,14 @@ class MergeFeature(object):
         if isinstance(new_r, graph.Constant):
             self.process_constant(fgraph, new_r)
 
-    def on_import(self, fgraph, node):
+    def on_import(self, fgraph, node, reason):
         for c in node.inputs:
             if isinstance(c, graph.Constant):
                 self.process_constant(fgraph, c)
 
         self.process_node(fgraph, node)
 
-    def on_prune(self, fgraph, node):
+    def on_prune(self, fgraph, node, reason):
         self.nodes_seen.discard(node)
         for c in node.inputs:
             if isinstance(c, graph.Constant) and (len(c.clients) <= 1):
@@ -1165,10 +1165,10 @@ class NavigatorOptimizer(Optimizer):
 
         class Updater:
             if importer is not None:
-                def on_import(self, fgraph, node):
+                def on_import(self, fgraph, node, reason):
                     importer(node)
             if pruner is not None:
-                def on_prune(self, fgraph, node):
+                def on_prune(self, fgraph, node, reason):
                     pruner(node)
             if chin is not None:
                 def on_change_input(self, fgraph, node, i, r, new_r):
@@ -1357,7 +1357,7 @@ class ChangeTracker:
     def __init__(self):
         self.changed = False
 
-    def on_import(self, fgraph, node):
+    def on_import(self, fgraph, node, reason):
         self.changed = True
 
     def on_change_input(self, fgraph, node, i, r, new_r):

theano/gof/toolbox.py

 import sys
 import time
 
+from theano import config
 from theano.gof.python25 import partial
 from theano.gof.python25 import OrderedDict
-
 from theano.gof import graph
 
 
-
 class AlreadyThere(Exception):
     """Raised by a Feature's on_attach callback method if the FunctionGraph
     attempting to attach the feature already has a functionally identical
@@ -57,7 +56,7 @@ class Feature(object):
         functionality that it installed into the function_graph.
         """
 
-    def on_import(self, function_graph, node):
+    def on_import(self, function_graph, node, reason):
         """
         Called whenever a node is imported into function_graph, which is
         just before the node is actually connected to the graph.
@@ -66,7 +65,7 @@ class Feature(object):
         you should do this by implementing on_attach.
         """
 
-    def on_prune(self, function_graph, node):
+    def on_prune(self, function_graph, node, reason):
         """
         Called whenever a node is pruned (removed) from the function_graph,
         after it is disconnected from the graph.
@@ -98,11 +97,11 @@ class Bookkeeper(Feature):
 
     def on_attach(self, fgraph):
         for node in graph.io_toposort(fgraph.inputs, fgraph.outputs):
-            self.on_import(fgraph, node)
+            self.on_import(fgraph, node, "on_attach")
 
     def on_detach(self, fgraph):
         for node in graph.io_toposort(fgraph.inputs, fgraph.outputs):
-            self.on_prune(fgraph, node)
+            self.on_prune(fgraph, node, 'Bookkeeper.detach')
 
 
 class History(Feature):
@@ -199,11 +198,14 @@ class ReplaceValidate(History, Validator):
     def replace_validate(self, fgraph, r, new_r, reason=None):
         self.replace_all_validate(fgraph, [(r, new_r)], reason=reason)
 
-    def replace_all_validate(self, fgraph, replacements, reason=None):
+    def replace_all_validate(self, fgraph, replacements,
+                             reason=None, verbose=None):
         chk = fgraph.checkpoint()
+        if verbose is None:
+            verbose = config.optimizer_verbose
         for r, new_r in replacements:
             try:
-                fgraph.replace(r, new_r, reason=reason)
+                fgraph.replace(r, new_r, reason=reason, verbose=False)
             except Exception, e:
                 if ('The type of the replacement must be the same' not in
                     str(e) and 'does not belong to this FunctionGraph' not in str(e)):
@@ -219,6 +221,8 @@ class ReplaceValidate(History, Validator):
         except Exception, e:
             fgraph.revert(chk)
             raise
+        if verbose:
+            print reason, r, new_r
         return chk
 
     def replace_all_validate_remove(self, fgraph, replacements,
@@ -267,7 +271,7 @@ class NodeFinder(dict, Bookkeeper):
         del fgraph.get_nodes
         Bookkeeper.on_detach(self, fgraph)
 
-    def on_import(self, fgraph, node):
+    def on_import(self, fgraph, node, reason):
         try:
             self.setdefault(node.op, []).append(node)
         except TypeError:  # node.op is unhashable
@@ -280,7 +284,7 @@ class NodeFinder(dict, Bookkeeper):
                 print >> sys.stderr, 'OFFENDING node not hashable'
             raise e
 
-    def on_prune(self, fgraph, node):
+    def on_prune(self, fgraph, node, reason):
         try:
             nodes = self[node.op]
         except TypeError:  # node.op is unhashable
@@ -312,13 +316,13 @@ class PrintListener(Feature):
         if self.active:
             print "-- detaching from: ", fgraph
 
-    def on_import(self, fgraph, node):
+    def on_import(self, fgraph, node, reason):
         if self.active:
-            print "-- importing: %s" % node
+            print "-- importing: %s, reason: %s" % (node, reason)
 
-    def on_prune(self, fgraph, node):
+    def on_prune(self, fgraph, node, reason):
         if self.active:
-            print "-- pruning: %s" % node
+            print "-- pruning: %s, reason: %s" % (node, reason)
 
     def on_change_input(self, fgraph, node, i, r, new_r, reason=None):
         if self.active:

theano/sandbox/cuda/basic_ops.py

@@ -2953,7 +2953,6 @@ class GpuJoin(tensor.Join, GpuOp):
         axis = inputs[0]
         n_cndas = len(inputs[1:])
         input_1 = inputs[1]
-        axis = inputs[0]
         fail = sub['fail']
         out = out_[0]
 

theano/sandbox/linalg/ops.py

@@ -137,9 +137,9 @@ class HintsFeature(object):
         # Variable -> tuple(scalars) or None  (All tensor vars map to tuple)
         self.hints = {}
         for node in fgraph.toposort():
-            self.on_import(fgraph, node)
+            self.on_import(fgraph, node, "on_attach")
 
-    def on_import(self, fgraph, node):
+    def on_import(self, fgraph, node, reason):
         if node.outputs[0] in self.hints:
             # this is a revert, not really an import
             for r in node.outputs + node.inputs:

theano/sandbox/scan_module/scan_utils.py

@@ -338,7 +338,7 @@ def infer_shape(outs, inputs, input_shapes):
             # shape_feature.on_import does not actually use an fgraph
             # It will call infer_shape and set_shape appropriately
             dummy_fgraph = None
-            shape_feature.on_import(dummy_fgraph, out.owner)
+            shape_feature.on_import(dummy_fgraph, out.owner, reason="dummy")
 
     ret = []
     for o in outs:

theano/scalar/basic.py

@@ -183,6 +183,24 @@ class Scalar(Type):
 
     def dtype_specs(self):
         try:
+            # To help debug dtype/typenum problem, here is code to get
+            # the list of numpy typenum.  This list change between 32
+            # and 64 bit platform and probably also also between
+            # Windows and Linux.
+            # NOTE: equivalent type on a platform can have different typenum.
+            #     This is the source of all dtype/typenum problem found up to
+            #     now, as Theano always expect the exact typenum that
+            #     correspond to our supported dtype.
+            """
+            for dtype in ['int8', 'uint8', 'short', 'ushort', 'intc', 'uintc',
+                          'longlong', 'ulonglong', 'single', 'double',
+                          'longdouble', 'csingle', 'cdouble', 'clongdouble',
+                          'float32', 'float64', 'int8', 'int16', 'int32',
+                          'int64', 'uint8', 'uint16', 'uint32', 'uint64',
+                          'complex64', 'complex128', 'float', 'double',
+                          'int', 'uint']:
+                print dtype, np.zeros(1, dtype=dtype).dtype.num
+            """
             return {  # dtype: (py_type, c_type, cls_name)
                     'float32': (numpy.float32, 'npy_float32', 'Float32'),
                     'float64': (numpy.float64, 'npy_float64', 'Float64'),

theano/scan_module/scan.py

@@ -101,7 +101,7 @@ def scan(fn,
 
         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 same
-        as the order of ``output_info``. For any sequence or output the
+        as the order of ``outputs_info``. For any sequence or output the
         order of the time slices is the same as the one in which they have
         been given as taps. For example if one writes the following :
 
@@ -262,7 +262,7 @@ def scan(fn,
         outputs will have *0 rows*. If the value is negative, ``scan``
         will run backwards in time. If the ``go_backwards`` flag is already
         set and also ``n_steps`` is negative, ``scan`` will run forward
-        in time. If n stpes is not provided, ``scan`` will figure
+        in time. If n_steps is not provided, ``scan`` will figure
         out the amount of steps it should run given its input sequences.
 
 
@@ -817,7 +817,7 @@ def scan(fn,
     if as_while:
         tmp_dummy_f_outs -= 1
     if not (tmp_dummy_f_outs == n_outs or outs_info == []):
-        raise ValueError('Please provide None as output_info for '
+        raise ValueError('Please provide None as outputs_info for '
                          'any output that does not feed back into '
                          'scan (i.e. it behaves like a map) ')
 

theano/scan_module/scan_op.py

@@ -1581,8 +1581,30 @@ class Scan(PureOp):
             if not isinstance(x.type, DisconnectedType):
                 outer_inp_seqs.append(x[::-1])
 
-        outer_inp_seqs += [x[::-1] for x in self.outer_mitsot_outs(outs)]
-        outer_inp_seqs += [x[::-1] for x in self.outer_sitsot_outs(outs)]
+        if hasattr(inputs[0].tag, 'test_value'):
+            # Here we tests that the new scan input sequence all have
+            # the same shape[0]. This is a properties that the scan()
+            # fct add and we want to keep it for all Scan op.  This is
+            # used in T_Scan.test_grad_multiple_outs_taps to test
+            # that.
+            for taps, x in zip(self.mitsot_taps(),
+                               self.outer_mitsot_outs(outs)):
+                mintap = numpy.min(taps)
+                if hasattr(x[::-1][:mintap], 'test_value'):
+                    assert (x[::-1][:mintap].tag.test_value.shape[0] ==
+                            inputs[0].tag.test_value)
+            for x in self.outer_sitsot_outs(outs):
+                if hasattr(x[::-1][:-1].tag, 'test_value'):
+                    assert (x[::-1][:-1].tag.test_value.shape[0] ==
+                            inputs[0].tag.test_value)
+            for x in self.outer_nitsot_outs(outs):
+                if hasattr(x[::-1].tag, 'test_value'):
+                    assert (x[::-1].tag.test_value.shape[0] ==
+                            inputs[0].tag.test_value)
+        outer_inp_seqs += [x[::-1][:numpy.min(taps)]
+                           for taps, x in zip(self.mitsot_taps(),
+                                              self.outer_mitsot_outs(outs))]
+        outer_inp_seqs += [x[::-1][:-1] for x in self.outer_sitsot_outs(outs)]
         outer_inp_seqs += [x[::-1] for x in self.outer_nitsot_outs(outs)]
 
         inner_inp_seqs = self.inner_seqs(self_inputs)

theano/scan_module/scan_opt.py

@@ -144,7 +144,7 @@ def remove_constants_and_unused_inputs_scan(node):
         nw_info['n_seqs'] = nw_n_seqs
         # DEBUG CHECK
         nwScan = scan_op.Scan(nw_inner, op_outs, nw_info)
-        nw_outs = nwScan.make_node(*nw_outer).outputs
+        nw_outs = nwScan(*nw_outer, **dict(return_list=True))
         return nw_outs
     else:
         return False
@@ -227,7 +227,11 @@ class PushOutNonSeqScan(gof.Optimizer):
                                  'this on theano-users list'), x)
                     outside_ins = [x.type.filter_variable(y) for x, y in
                                    zip(nd.inputs, outside_ins)]
-                    nw_outer_node = nd.op.make_node(*outside_ins)
+
+                    # Do not call make_node for test_value
+                    nw_outer_node = nd.op(*outside_ins,
+                                          **dict(return_list=True))[0].owner
+
                     # Step 2. Create variables for replacements
                     for idx, y in enumerate(nd.outputs):
 
@@ -285,11 +289,15 @@ class PushOutNonSeqScan(gof.Optimizer):
             op_ins, op_outs = scan_utils.reconstruct_graph(_op_ins, _op_outs)
             # Reconstruct node
             nwScan = scan_op.Scan(op_ins, op_outs, op.info)
-            nw_node = nwScan.make_node(* (node.inputs + nw_outer))
+
+            # Do not call make_node for test_value
+            nw_node = nwScan(*(node.inputs + nw_outer),
+                             **dict(return_list=True))[0].owner
+
             fgraph.replace_all_validate_remove(
                 zip(node.outputs, nw_node.outputs),
                 remove=[node],
-                reason='scan_push_computation_out')
+                reason='scanOp_pushout_nonseqs_ops')
             return True
         elif to_keep == []:
             # Nothing in the inner graph should be kept
@@ -310,7 +318,7 @@ class PushOutNonSeqScan(gof.Optimizer):
                 fgraph.replace_all_validate_remove(
                     replace_with.items(),
                     remove=[node],
-                    reason='scan_push_computation_out')
+                    reason='scanOp_pushout_nonseqs_ops')
 
         else:
             return False
@@ -327,8 +335,8 @@ class PushOutSeqScan(gof.Optimizer):
         fgraph.attach_feature(gof.toolbox.ReplaceValidate())
 
     def apply(self, fgraph):
-        nodelist = [x for x in fgraph.toposort() if isinstance(x.op,
-                                                           scan_op.Scan)]
+        nodelist = [x for x in fgraph.toposort()
+                    if isinstance(x.op, scan_op.Scan)]
         for node in nodelist:
             self.process_node(fgraph, node)
 
@@ -376,18 +384,21 @@ class PushOutSeqScan(gof.Optimizer):
                         elif x in inner_seqs:
                             outside_ins += [outer_seqs[inner_seqs.index(x)]]
                         elif x in to_replace:
-                            outside_ins += [replace_with_out[\
+                            outside_ins += [replace_with_out[
                                 to_replace.index(x)]]
                         elif isinstance(x, theano.Constant):
                             outside_ins += [x.clone()]
                         else:
                             raise Exception(
-                                ('Error in the `scan_pushout_non_seq_'
+                                ('Error in the `scan_pushout_seq_'
                                  'operations`. The optimization tries '
                                  'to move some computation fron scan '
                                  'which is not allowed to move. Report '
                                  'this on theano-users list'), x)
-                    nw_outer_node = nd.op.make_node(*outside_ins)
+                    # Do not call make_node for test_value
+                    nw_outer_node = nd.op(*outside_ins,
+                                          **dict(return_list=True))[0].owner
+
                     # Step 2. Create variables for replacements
                     for idx, y in enumerate(nd.outputs):
 
@@ -420,10 +431,15 @@ class PushOutSeqScan(gof.Optimizer):
                     to_replace += [y]
                     replace_with_in += [y_place_holder]
                     replace_with_out += [new_outer]
+                    if hasattr(new_outer.tag, "test_value"):
+                        new_sh = new_outer.tag.test_value.shape
+                        ref_sh = (outside_ins.tag.test_value.shape[0],)
+                        ref_sh += nd.outputs[0].tag.test_value.shape
+                        assert new_sh == ref_sh
 
                     changed = True
         if counts >= max_iterations:
-            raise Exception('Error in the `scan_pushout_non_seq_operations`.'
+            raise Exception('Error in the `scan_pushout_seq_operations`.'
                             ' The optimization exhausted the maximal number '
                             'of iterations allowed!')
         # We need to check all candidate replacements and choose those that
@@ -473,12 +489,14 @@ class PushOutSeqScan(gof.Optimizer):
             nw_info = op.info.copy()
             nw_info['n_seqs'] += len(nw_inner)
             nwScan = scan_op.Scan(op_ins, op_outs, nw_info)
-            nw_node = nwScan.make_node(* (node.inputs[:1] + nw_outer +
-                                          node.inputs[1:]))
+            # Do not call make_node for test_value
+            nw_node = nwScan(*(node.inputs[:1] + nw_outer + node.inputs[1:]),
+                             **dict(return_list=True))[0].owner
+
             fgraph.replace_all_validate_remove(
                 zip(node.outputs, nw_node.outputs),
                 remove=[node],
-                reason='scan_push_computation_out')
+                reason='scanOp_pushout_seqs_ops')
             return True
         elif (to_keep == [] and
               not op.as_while and
@@ -510,8 +528,8 @@ class PushOutSeqScan(gof.Optimizer):
                 fgraph.replace_all_validate_remove(
                     replace_with.items(),
                     remove=[node],
-                    reason='scan_push_seq_computation_out')
-
+                    reason='scanOp_pushout_seqs_ops')
+                return True
         else:
             return False
 
@@ -563,12 +581,13 @@ class ScanInplaceOptimizer(Optimizer):
                                       info,
                                       typeConstructor=self.typeConstructor)
 
-                new_outs = new_op.make_node(*inputs).outputs
+                # Do not call make_node for test_value
+                new_outs = new_op(*inputs, **dict(return_list=True))
                 try:
                     fgraph.replace_all_validate_remove(
                         zip(node.outputs, new_outs),
                         remove=[node],
-                        reason=self.__class__.__name__)
+                        reason='scanOp_make_inplace')
                     op = new_op
                     node = new_outs[0].owner
                 except InconsistencyError, e:
@@ -847,9 +866,8 @@ class ScanSaveMem(gof.Optimizer):
             nw_inputs[0] = nw_steps
 
             # 3.2 check orphane outputs to see if we can eliminate any
-            required, not_required = \
-                    scan_utils.scan_can_remove_outs(node.op,
-                                                    orphane_outs)
+            required, not_required = scan_utils.scan_can_remove_outs(
+                node.op, orphane_outs)
             # 3.3. compose replace pairs for those nodes that need not
             # to store everything in memory ( or ar orphane and required
             # by the inner function .. )
@@ -947,9 +965,10 @@ class ScanSaveMem(gof.Optimizer):
             # I need to make sure I'm not reapplying the same optimization
             # twice since bad things usually happen if I do that
             info['_scan_savemem_visited'] = True
-            new_outs = scan_op.Scan(inps,
-                                    outs,
-                                    info).make_node(*node_ins).outputs
+
+            # Do not call make_node for test_value
+            new_outs = scan_op.Scan(inps, outs, info)(*node_ins,
+                                                      **dict(return_list=True))
 
             old_new = []
             # 3.7 Get replace pairs for those outputs that do not change
@@ -979,8 +998,7 @@ class ScanSaveMem(gof.Optimizer):
                             nw_slice,
                             lambda entry: isinstance(entry,
                                                      tensor.Variable))
-                        new_o = subtens.make_node(new_outs[nw_pos],
-                                                  *sl_ins).outputs[0]
+                        new_o = subtens(new_outs[nw_pos], *sl_ins)
                         if new_o.ndim > 0:
                             new_o = new_o[::cnf_slice[1]]
                         replaced_outs.append(idx)
@@ -1011,16 +1029,14 @@ class ScanSaveMem(gof.Optimizer):
                             position = (cnf_slice[0] - nw_steps -
                                         init_l[pos] + store_steps[pos])
 
-                            nw_slice = (sanitize(position),) + \
-                                    tuple(old_slices[1:])
-
+                            nw_slice = (sanitize(position),) + tuple(
+                                old_slices[1:])
                         subtens = tensor.Subtensor(nw_slice)
                         sl_ins = tensor.Subtensor.collapse(
                             nw_slice,
                             lambda entry: isinstance(entry,
                                                      tensor.Variable))
-                        new_o = subtens.make_node(new_outs[nw_pos],
-                                                  *sl_ins).outputs[0]
+                        new_o = subtens(new_outs[nw_pos], *sl_ins)
                         if new_o.ndim > 0:
                             new_o = new_o[::cnf_slice[1]]
                         old_new += [(old, new_o)]
@@ -1042,7 +1058,7 @@ class ScanSaveMem(gof.Optimizer):
                 remove.append(node)
                 fgraph.replace_all_validate_remove(old_new,
                                                    remove,
-                                                   reason='scan_save_mem')
+                                                   reason='scanOp_save_mem')
 
     def apply(self, fgraph):
 
@@ -1230,7 +1246,7 @@ class ScanMerge(gof.Optimizer):
                 proposal = self.merge(subset)
                 fgraph.replace_all_validate_remove(proposal,
                                                    remove=subset,
-                                                   reason='scan_merge')
+                                                   reason='scanOp_merge')
 
 
 def has_duplicates(l):
@@ -1592,10 +1608,8 @@ class PushOutDot1(gof.Optimizer):
                         old = node.outputs[pos].clients[0][0].outputs[0]
                         old_new.append((old, new_out))
                         old_new += zip(node.outputs[pos+1:], new_outs[pos:])
-                        fgraph.replace_all_validate_remove(old_new,
-                                                   remove = [node],
-                                                   reason='PushOutDot1')
-
+                        fgraph.replace_all_validate_remove(
+                            old_new, remove=[node], reason='scan_pushout_dot1')
 
 
 # I've added an equilibrium because later scan optimization in the sequence
@@ -1628,6 +1642,7 @@ scan_seqopt1.register('scanOp_remove_constants_and_unused_inputs0',
                       opt.in2out(remove_constants_and_unused_inputs_scan,
                                  ignore_newtrees=True),
                       1,
+                      'remove_constants_and_unused_inputs_scan',
                       'fast_run',
                       'scan')
 
@@ -1662,10 +1677,11 @@ scan_seqopt2.register('constant_folding_for_scan2',
                       'scan')
 
 
-scan_seqopt2.register('scanOp_remove_constants_and_unused_inputs0',
+scan_seqopt2.register('scanOp_remove_constants_and_unused_inputs1',
                       opt.in2out(remove_constants_and_unused_inputs_scan,
                                  ignore_newtrees=True),
                       2,
+                      'remove_constants_and_unused_inputs_scan',
                       'fast_run',
                       'scan')
 
@@ -1684,12 +1700,14 @@ scan_seqopt2.register('scanop_remove_constants_and_unused_inputs2',
                       opt.in2out(remove_constants_and_unused_inputs_scan,
                                  ignore_newtrees=True),
                       5,
+                      'remove_constants_and_unused_inputs_scan',
                       'fast_run',
                       'scan')
 
 scan_seqopt2.register('scanOp_merge_inouts',
                       opt.in2out(scan_merge_inouts, ignore_newtrees=True),
                       6,
+                      'scan_merge_inouts',
                       'fast_run',
                       'scan')
 
@@ -1707,5 +1725,6 @@ scan_seqopt2.register('scanOp_remove_constants_and_unused_inputs3',
                       opt.in2out(remove_constants_and_unused_inputs_scan,
                                  ignore_newtrees=True),
                       8,
+                      'remove_constants_and_unused_inputs_scan',
                       'fast_run',
                       'scan')

theano/scan_module/scan_utils.py

@@ -500,7 +500,7 @@ def infer_shape(outs, inputs, input_shapes):
             # shape_feature.on_import does not actually use an fgraph
             # It will call infer_shape and set_shape appropriately
             dummy_fgraph = None
-            shape_feature.on_import(dummy_fgraph, out.owner)
+            shape_feature.on_import(dummy_fgraph, out.owner, reason="dummy")
 
     ret = []
     for o in outs:

theano/scan_module/tests/test_scan.py

@@ -1545,6 +1545,12 @@ class T_Scan(unittest.TestCase):
         x0 = theano.tensor.vector('x0')
         y0 = theano.tensor.vector('y0')
 
+        W_in1.tag.test_value = vW_in1
+        u1.tag.test_value = v_u1
+        u2.tag.test_value = v_u2
+        x0.tag.test_value = v_x0
+        y0.tag.test_value = v_y0
+
         def f_rnn_cmpl(u1_t,
                        u2_tm1,
                        u2_t,
@@ -1553,11 +1559,21 @@ class T_Scan(unittest.TestCase):
                        y_tm1,
                        y_tm3,
                        W_in1):
-            return [theano.dot(u1_t, W_in1) + \
-                      (u2_t + u2_tm1 * u2_tp1) * W_in2 + \
+            return [theano.dot(u1_t, W_in1) +
+                    (u2_t + u2_tm1 * u2_tp1) * W_in2 +
                     theano.dot(x_tm1, W),
                     (y_tm1 + y_tm3) * theano.dot(x_tm1, W_out),
                     theano.dot(u1_t, W_in1)]
+
+        # We change the compute_test_value[_opt] flag to run the
+        # assert in Scan.grad() of the new scan input sequence related
+        # to outer_mitsot_outs, outer_sitsot_outs and
+        # outer_nitsot_outs. This allow to test an old Scan bug.
+        old1 = theano.config.compute_test_value
+        old2 = theano.config.compute_test_value_opt
+        theano.config.compute_test_value = 'raise'
+        theano.config.compute_test_value_opt = 'raise'
+        try:
             cost, updates = scan_project_sum(
                 f_rnn_cmpl,
                 [u1, dict(input=u2, taps=[-1, 0, 1])],
@@ -1580,6 +1596,9 @@ class T_Scan(unittest.TestCase):
                                       updates=updates,
                                       no_default_updates=True,
                                       allow_input_downcast=True)
+        finally:
+            theano.config.compute_test_value = old1
+            theano.config.compute_test_value_opt = old2
 
         num_grad = multiple_outputs_numeric_grad(cost_fn,
                                                  [v_u1,

theano/tensor/basic.py

@@ -2543,7 +2543,7 @@ class Alloc(gof.Op):
         #change.
         return [gx] + [DisconnectedType()() for i in inputs[1:]]
 
-    def __call__(self, val, *shapes):
+    def __call__(self, val, *shapes, **kwargs):
         """
         If the alloc would be useless, this function returns val.
 
@@ -2554,7 +2554,7 @@ class Alloc(gof.Op):
 
         If you always want an Alloc node, call make_node.
         """
-        ret = super(Alloc, self).__call__(val, *shapes)
+        ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
         try:
             # It makes optimization difficult when useless allocs are thrown
             # into the graph at every stage of optimization.  This little logic

theano/tensor/opt.py

@@ -49,14 +49,24 @@ theano.configparser.AddConfigVar('on_shape_error',
 
 def out2in(*local_opts):
     """WRITEME """
-    return opt.TopoOptimizer(opt.LocalOptGroup(*local_opts),
+    if len(local_opts) > 1:
+        # Don't wrap it uselessly if their is only 1 optimization.
+        local_opts = opt.LocalOptGroup(*local_opts),
+    else:
+        local_opts, = local_opts
+    return opt.TopoOptimizer(local_opts,
                              order='out_to_in',
                              failure_callback=TopoOptimizer.warn_inplace)
 
 
 def in2out(*local_opts, **kwargs):
     """WRITEME """
-    return opt.TopoOptimizer(opt.LocalOptGroup(*local_opts),
+    if len(local_opts) > 1:
+        # Don't wrap it uselessly if their is only 1 optimization.
+        local_opts = opt.LocalOptGroup(*local_opts),
+    else:
+        local_opts, = local_opts
+    return opt.TopoOptimizer(local_opts,
                              order='in_to_out',
                              failure_callback=TopoOptimizer.warn_inplace,
                              **kwargs)
@@ -384,10 +394,12 @@ def local_dimshuffle_lift(node):
     input = node.inputs[0]
     inode = input.owner
     if inode and isinstance(inode.op, Elemwise) and (len(input.clients) == 1):
-        return inode.op.make_node(*[DimShuffle(input.type.broadcastable,
+        # Don't use make_node to have tag.test_value set.
+        ret = inode.op(*[DimShuffle(input.type.broadcastable,
                                     op.new_order,
                                     op.inplace)(input) for input in
-                                    inode.inputs]).outputs
+                         inode.inputs], **dict(return_list=True))
+        return ret
     if inode and isinstance(inode.op, DimShuffle):
         new_order = [x == 'x' and 'x' or inode.op.new_order[x] for x in
                      op.new_order]
@@ -397,8 +409,9 @@ def local_dimshuffle_lift(node):
                                                    iinput.type.ndim):
             return [iinput]
         else:
-            return DimShuffle(iinput.type.broadcastable, new_order,
-                              inplace).make_node(iinput).outputs
+            ret = DimShuffle(iinput.type.broadcastable, new_order,
+                             inplace)(iinput, **dict(return_list=True))
+            return ret
 
 
 @register_canonicalize
@@ -437,8 +450,10 @@ def dimshuffle_as_view(node):
 
 #Step 60 is the inplace optimization stage.
 compile.optdb.register('dimshuffle_as_view',
-                       TopoOptimizer(dimshuffle_as_view,
-    failure_callback=TopoOptimizer.warn_inplace), 60,
+                       TopoOptimizer(
+                           dimshuffle_as_view,
+                           failure_callback=TopoOptimizer.warn_inplace),
+                       60,
                        'fast_run', 'inplace')
 register_canonicalize(local_dimshuffle_lift)
 register_specialize(local_dimshuffle_lift)
@@ -771,7 +786,8 @@ class ShapeFeature(object):
         if hasattr(r.type, "broadcastable") and r.type.broadcastable[i]:
             return self.lscalar_one
         else:
-            return Shape_i(i).make_node(r).outputs[0]
+            # Do not call make_node for test_value
+            return Shape_i(i)(r)
 
     def shape_tuple(self, r):
         """Return a tuple of symbolic shape vars for tensor variable r"""
@@ -970,9 +986,9 @@ class ShapeFeature(object):
         # shape var -> graph v
 
         for node in fgraph.toposort():
-            self.on_import(fgraph, node)
+            self.on_import(fgraph, node, reason='on_attach')
 
-    def on_import(self, fgraph, node):
+    def on_import(self, fgraph, node, reason):
         if node.outputs[0] in self.shape_of:
             # this is a revert, not really an import
             for r in node.outputs + node.inputs:
@@ -1933,7 +1949,8 @@ def local_subtensor_merge(node):
             sl_ins = Subtensor.collapse(
                 merged_slices,
                 lambda x: isinstance(x, T.Variable))
-            out = subtens.make_node(x, *sl_ins).outputs[0]
+            # Do not call make_node for test_value
+            out = subtens(x, *sl_ins)
 
             return [out]
 
@@ -4583,8 +4600,12 @@ def local_elemwise_fusion_op(OP, max_input_fct=lambda node: 1024):
                         elif ii in tmp_input:
                             tmp_s_input.append(tmp_scalar[tmp_input.index(ii)])
                         else:
-                            tmp_s_input.append(scalar.Scalar(
-                                    ii.dtype).make_variable())
+                            tmp = scalar.Scalar(ii.dtype).make_variable()
+                            try:
+                                tmp.tag.test_value = gof.op.get_test_value(ii).flatten()[0]
+                            except AttributeError:
+                                pass
+                            tmp_s_input.append(tmp)
                             tmp_input.append(ii)
                             tmp_scalar.append(tmp_s_input[-1])
                     s_op = i.owner.op.scalar_op(*tmp_s_input)
@@ -4634,6 +4655,13 @@ def local_elemwise_fusion_op(OP, max_input_fct=lambda node: 1024):
                     s = s_inputs[inputs.index(i)]
                 else:
                     s = scalar.Scalar(i.dtype).make_variable()
+                    try:
+                        v = gof.op.get_test_value(i)
+                        if v.size > 0:
+                            s.tag.test_value = gof.op.get_test_value(i).flatten()[0]
+                    except AttributeError:
+                        pass
+
                     inputs.append(i)
                     s_inputs.append(s)
                 s_g.append(s)
@@ -4667,7 +4695,8 @@ your code will run correctly, but may be slower.""")
         C = scalar.Composite(s_inputs, [s_new_out])
 
         #create the new node.
-        n = OP(C).make_node(*inputs)
+        #Do not call make_node to have test_value
+        n = OP(C)(*inputs).owner
         assert len(n.outputs) == 1
         assert node.outputs[0].dtype == n.outputs[0].dtype
 
@@ -4728,9 +4757,11 @@ if config.tensor.local_elemwise_fusion:
     _logger.debug("enabling optimization fusion elemwise in fast_run")
     compile.optdb.register('elemwise_fusion',
                            FusionOptimizer(local_elemwise_fusion), 71.00,
-                           'fast_run', 'fusion', 'local_elemwise_fusion')
+                           'fast_run', 'fusion', 'local_elemwise_fusion',
+                           'FusionOptimizer')
 else:
     _logger.debug("not enabling optimization fusion elemwise in fast_run")
     compile.optdb.register('elemwise_fusion',
                            FusionOptimizer(local_elemwise_fusion), 71.00,
-                           'fusion', 'local_elemwise_fusion')
+                           'fusion', 'local_elemwise_fusion',
+                           'FusionOptimizer')