Merge pull request #1556 from nouiz/mixed2

doc/library/sparse/index.txt

@@ -123,6 +123,7 @@ List of Implemented Operations
       Both grad are implemented. Structured by default.
     - :class:`SparseFromDense <theano.sparse.basic.SparseFromDense>` and ``csr_from_dense``, ``csc_from_dense``.
       The grad implemented is structured.
+    - Theano SparseVariable object have a method ``toarray()`` that is the same as ``dense_from_sparse``.
 
 - Construction of Sparses and their Properties
     - :class:`CSM <theano.sparse.basic.CSM>` and ``CSC``, ``CSR`` to construct a matrix.

theano/gof/compiledir.py

@@ -208,6 +208,8 @@ def cleanup():
                         have_c_compiler = False
                         for obj in flatten(key):
                             if isinstance(obj, numpy.ndarray):
+                                #Reuse have_npy_abi_version to
+                                #force the removing of key
                                 have_npy_abi_version = False
                                 break
                             elif isinstance(obj, basestring):
@@ -219,6 +221,8 @@ def cleanup():
                                   hasattr(obj, 'c_code_cache_version')):
                                 v = obj.c_code_cache_version()
                                 if v not in [(), None] and v not in key[0]:
+                                    #Reuse have_npy_abi_version to
+                                    #force the removing of key
                                     have_npy_abi_version = False
                                     break
 

theano/gof/destroyhandler.py

@@ -442,7 +442,7 @@ if 0:
 
             self.stale_droot = True
 
-        def on_change_input(self, fgraph, app, i, old_r, new_r):
+        def on_change_input(self, fgraph, app, i, old_r, new_r, reason):
             """app.inputs[i] changed from old_r to new_r """
             if app == 'output':
                 # app == 'output' is special key that means FunctionGraph is redefining which nodes are being
@@ -827,7 +827,7 @@ class DestroyHandler(toolbox.Bookkeeper):
 
         self.stale_droot = True
 
-    def on_change_input(self, fgraph, app, i, old_r, new_r):
+    def on_change_input(self, fgraph, app, i, old_r, new_r, reason):
         """app.inputs[i] changed from old_r to new_r """
         if app == 'output':
             # app == 'output' is special key that means FunctionGraph is redefining which nodes are being

theano/gof/fg.py

@@ -376,7 +376,7 @@ class FunctionGraph(utils.object2):
         current value of node.inputs[i] which we want to replace.
 
         For each feature that has a 'on_change_input' method, calls:
-          feature.on_change_input(function_graph, node, i, old_r, new_r, [reason])
+          feature.on_change_input(function_graph, node, i, old_r, new_r, reason)
         """
         # TODO: ERROR HANDLING FOR LISTENERS (should it complete the change or revert it?)
         if node == 'output':
@@ -512,14 +512,7 @@ class FunctionGraph(utils.object2):
                 # not existing
                 continue
 
-            #####HORRIBLE OPTIONAL ARGUMENT HACK
-            try:
-                fn(self, *args, **kwargs)
-            except TypeError, e:
-                if str(e) == "on_change_input() got an unexpected keyword argument 'reason'" and len(kwargs) == 1:
-                    fn(self, *args)
-                else:
-                    raise
+            fn(self, *args, **kwargs)
 
     def collect_callbacks(self, name, *args):
         """WRITEME

theano/gof/opt.py

@@ -423,7 +423,7 @@ class MergeFeature(object):
         for node in fgraph.toposort():
             self.on_import(fgraph, node, "on_attach")
 
-    def on_change_input(self, fgraph, node, i, r, new_r):
+    def on_change_input(self, fgraph, node, i, r, new_r, reason):
         # If inputs to node change, it is not guaranteed that it is distinct
         # from the other nodes in nodes_seen
         if node in self.nodes_seen:
@@ -555,6 +555,9 @@ class MergeOptimizer(Optimizer):
         # clear blacklist
         fgraph.merge_feature.blacklist = []
 
+    def __str__(self):
+        return self.__class__.__name__
+
 merge_optimizer = MergeOptimizer()
 
 
@@ -1171,7 +1174,7 @@ class NavigatorOptimizer(Optimizer):
                 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):
+                def on_change_input(self, fgraph, node, i, r, new_r, reason):
                     chin(node, i, r, new_r)
 
         u = Updater()
@@ -1229,7 +1232,7 @@ class NavigatorOptimizer(Optimizer):
         # If an output would be replaced by itself, no need to perform
         # the replacement
         repl_pairs = [(r, rnew) for r, rnew in zip(node.outputs, replacements)
-                if rnew is not r]
+                      if rnew is not r]
         if len(repl_pairs) == 0:
             return False
         try:
@@ -1302,6 +1305,10 @@ class TopoOptimizer(NavigatorOptimizer):
             raise
         self.detach_updater(fgraph, u)
 
+    def __str__(self):
+        return getattr(self, '__name__',
+                       '<TopoOptimizer instance>')
+
 
 class OpKeyOptimizer(NavigatorOptimizer):
     """WRITEME"""
@@ -1360,7 +1367,7 @@ class ChangeTracker:
     def on_import(self, fgraph, node, reason):
         self.changed = True
 
-    def on_change_input(self, fgraph, node, i, r, new_r):
+    def on_change_input(self, fgraph, node, i, r, new_r, reason):
         self.changed = True
 
     def reset(self):
@@ -1415,23 +1422,29 @@ class EquilibriumOptimizer(NavigatorOptimizer):
     def apply(self, fgraph, start_from=None):
         if start_from is None:
             start_from = fgraph.outputs
+        else:
+            for node in start_from:
+                assert node in fgraph.outputs
+
         changed = True
         max_use_abort = False
         opt_name = None
-        process_count = {}
+        global_process_count = {}
         max_nb_nodes = len(fgraph.apply_nodes)
         max_use = max_nb_nodes * self.max_use_ratio
 
         loop_timing = []
+        loop_process_count = []
         global_opt_timing = []
         time_opts = {}
         io_toposort_timing = []
         nb_nodes = []
         for opt in self.global_optimizers + self.local_optimizers:
-            process_count.setdefault(opt, 0)
+            global_process_count.setdefault(opt, 0)
             time_opts.setdefault(opt, 0)
 
         while changed and not max_use_abort:
+            process_count = {}
             t0 = time.time()
             changed = False
 
@@ -1442,9 +1455,11 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                 gopt.apply(fgraph)
                 time_opts[gopt] += time.time() - t_opt
                 if fgraph.change_tracker.changed:
+                    process_count.setdefault(gopt, 0)
                     process_count[gopt] += 1
+                    global_process_count[gopt] += 1
                     changed = True
-                    if process_count[gopt] > max_use:
+                    if global_process_count[gopt] > max_use:
                         max_use_abort = True
                         opt_name = (getattr(gopt, "name", None)
                                     or getattr(gopt, "__name__", ""))
@@ -1452,9 +1467,6 @@ class EquilibriumOptimizer(NavigatorOptimizer):
             global_opt_timing.append(float(time.time() - t0))
 
             #apply local optimizer
-            for node in start_from:
-                assert node in fgraph.outputs
-
             topo_t0 = time.time()
             q = deque(graph.io_toposort(fgraph.inputs, start_from))
             io_toposort_timing.append(time.time() - topo_t0)
@@ -1485,9 +1497,11 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                         lopt_change = self.process_node(fgraph, node, lopt)
                         time_opts[lopt] += time.time() - t_opt
                         if lopt_change:
+                            process_count.setdefault(lopt, 0)
                             process_count[lopt] += 1
+                            global_process_count[lopt] += 1
                             changed = True
-                            if process_count[lopt] > max_use:
+                            if global_process_count[lopt] > max_use:
                                 max_use_abort = True
                                 opt_name = (getattr(lopt, "name", None)
                                             or getattr(lopt, "__name__", ""))
@@ -1497,6 +1511,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
             finally:
                 self.detach_updater(fgraph, u)
 
+            loop_process_count.append(process_count)
             loop_timing.append(float(time.time() - t0))
 
         if max_use_abort:
@@ -1505,7 +1520,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                           + "%f with the theano flag 'optdb.max_use_ratio'." %
                           config.optdb.max_use_ratio)
 
-        return (self, loop_timing, process_count, max_nb_nodes,
+        return (self, loop_timing, loop_process_count, max_nb_nodes,
                 global_opt_timing, nb_nodes, time_opts, io_toposort_timing)
 
     def print_summary(self, stream=sys.stdout, level=0, depth=-1):
@@ -1519,40 +1534,68 @@ class EquilibriumOptimizer(NavigatorOptimizer):
 
     @staticmethod
     def print_profile(stream, prof, level=0):
-        (opt, loop_timing, process_count, max_nb_nodes,
+        (opt, loop_timing, loop_process_count, max_nb_nodes,
          global_opt_timing, nb_nodes, time_opts, io_toposort_timing) = prof
+
         blanc = ('    ' * level)
         print >> stream, blanc, "EquilibriumOptimizer",
         print >> stream, blanc, getattr(opt, "name",
                                         getattr(opt, "__name__", ""))
-        print >> stream, blanc, " time %.3fs for %d passes, %d nodes max" % (
+        print >> stream, blanc, "  time %.3fs for %d passes, %d nodes max" % (
                 sum(loop_timing), len(loop_timing), max_nb_nodes)
-        print >> stream, blanc, " time io_toposort %.3fs" % sum(
+        print >> stream, blanc, "  time io_toposort %.3fs" % sum(
             io_toposort_timing)
+        s = sum([time_opts[o] for o in opt.local_optimizers])
+        print >> stream, blanc, "  time in local optimizers %.3fs" % s
+        s = sum([time_opts[o] for o in opt.global_optimizers])
+        print >> stream, blanc, "  time in global optimizers %.3fs" % s
         for i in range(len(loop_timing)):
-            print >> stream, blanc, ('%d - %.3fs (%.3fs in global opts, '
-                                     '%.3fs io_toposort) - %d nodes' % (
+            lopt = ""
+            if loop_process_count[i]:
+                d = list(reversed(sorted(loop_process_count[i].iteritems(),
+                                         key=lambda a: a[1])))
+                lopt = " ".join([str((str(k), v)) for k, v
+                                 in d[:5]])
+                if len(d) > 5:
+                    lopt += " ..."
+            print >> stream, blanc, ('  %2d - %.3fs %d (%.3fs in global opts, '
+                                     '%.3fs io_toposort) - %d nodes - %s' % (
                                          i, loop_timing[i],
+                                         sum(loop_process_count[i].values()),
                                          global_opt_timing[i],
-                                         io_toposort_timing[i], nb_nodes[i]))
+                                         io_toposort_timing[i], nb_nodes[i],
+                                         lopt))
 
         count_opt = []
+        not_used = 0
+        not_used_time = 0
+        process_count = {}
+        for o in opt.global_optimizers + opt.local_optimizers:
+            process_count.setdefault(o, 0)
+        for count in loop_process_count:
+            for o, v in count.iteritems():
+                process_count[o] += v
         for opt, count in process_count.iteritems():
             if count > 0:
                 count_opt.append((time_opts[opt], count, opt))
+            else:
+                not_used += 1
+                not_used_time += time_opts[opt]
 
         if count_opt:
             print >> stream, blanc, \
-                    'times applied - optimizer (only those applied):'
+                    '  times - times applied - name:'
             count_opt.sort()
             for (t, count, opt) in count_opt[::-1]:
                 print >> stream, blanc, '  %.3fs - %d - %s' % (
                     t, count, opt)
+            print >> stream, blanc, '  %.3fs - in %d optimization that where not used' % (
+                not_used_time, not_used)
             print >> stream
 
     @staticmethod
     def merge_profile(prof1, prof2):
-        #(opt, loop_timing, process_count, max_nb_nodes,
+        #(opt, loop_timing, loop_process_count, max_nb_nodes,
         # global_opt_timing, nb_nodes, time_opts, io_toposort_timing) = prof1
 
         local_optimizers = set(prof1[0].local_optimizers).union(
@@ -1574,12 +1617,16 @@ class EquilibriumOptimizer(NavigatorOptimizer):
 
         loop_timing = merge_list(prof1[1], prof2[1])
 
-        process_count = prof1[2].copy()
-        for process, count in prof2[2].iteritems():
-            if process in process_count:
-                process_count[process] += count
-            else:
-                process_count[process] = count
+        loop_process_count = prof1[2].copy()
+        for i in range(len(loop_process_count)):
+            process_count = loop_process_count[i]
+            for process, count in prof2[2][i].iteritems():
+                if process in process_count:
+                    process_count[process] += count
+                else:
+                    process_count[process] = count
+        for i in range(len(loop_process_count), len(prof2[2])):
+            loop_process_count.append(prof2[2].copy())
 
         max_nb_nodes = max(prof1[3], prof2[3])
 
@@ -1601,7 +1648,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         assert len(loop_timing) == max(len(prof1[1]), len(prof2[1]))
         return (new_opt,
                 loop_timing,
-                process_count,
+                loop_process_count,
                 max_nb_nodes,
                 global_opt_timing,
                 nb_nodes,

theano/sandbox/linalg/ops.py

@@ -159,7 +159,7 @@ class HintsFeature(object):
             if k not in new_hints:
                 new_hints[k] = v
 
-    def on_change_input(self, fgraph, node, i, r, new_r):
+    def on_change_input(self, fgraph, node, i, r, new_r, reason):
         # TODO:
         # This tells us that r and new_r must have the same shape
         # if we didn't know that the shapes are related, now we do.

theano/sparse/basic.py

@@ -300,6 +300,8 @@ class _sparse_py_operators:
     # def _as_TensorVariable(self):
     #    return dense_from_sparse(self)
 
+    def toarray(self):
+        return dense_from_sparse(self)
     shape = property(lambda self: tensor.shape(dense_from_sparse(self)))
     # don't worry!
     # the plan is that the ShapeFeature in tensor.opt will do shape propagation
@@ -1843,6 +1845,8 @@ class AddSD(gof.op.Op):
     def infer_shape(self, node, shapes):
         return [shapes[3]]
 
+    def c_code_cache_version(self):
+        return (1,)
 add_s_d = AddSD()
 
 
@@ -1918,6 +1922,10 @@ def add(x, y):
         x = as_sparse_variable(x)
     if hasattr(y, 'getnnz'):
         y = as_sparse_variable(y)
+    if not isinstance(x, theano.Variable):
+        x = theano.tensor.as_tensor_variable(x)
+    if not isinstance(y, theano.Variable):
+        y = theano.tensor.as_tensor_variable(y)
 
     x_is_sparse_variable = _is_sparse_variable(x)
     y_is_sparse_variable = _is_sparse_variable(y)

theano/sparse/tests/test_basic.py

@@ -567,68 +567,58 @@ class T_AddMul(unittest.TestCase):
     def _testSD(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]),
                 array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])):
         for mtype in _mtypes:
-            a = numpy.array(array1)
-            aR = tensor.as_tensor_variable(a)
-            self.assertFalse(aR.data is a)  # constants are copied
-            self.assertTrue(_is_dense(a))
-            self.assertTrue(_is_dense_variable(aR))
-
-            b = mtype(array2)
-            bR = as_sparse_variable(b)
-            self.assertFalse(bR.data is b)  # constants are copied
-            self.assertTrue(_is_sparse(b))
-            self.assertTrue(_is_sparse_variable(bR))
-
-            apb = op(aR, bR)
-
-            self.assertTrue(apb.type.dtype == aR.type.dtype, apb.type.dtype)
-            self.assertTrue(apb.type.dtype == bR.type.dtype, apb.type.dtype)
-
-            val = eval_outputs([apb])
-            self.assertTrue(val.shape == (3, 2))
-            if op is add:
-                self.assertTrue(_is_dense_variable(apb))
-                self.assertTrue(numpy.all(val == (a + b)))
-                ans = numpy.array([[1., 2], [3, 4], [5, 6]])
-                self.assertTrue(numpy.all(val == ans))
-            elif op is mul:
-                self.assertTrue(_is_sparse_variable(apb))
-                self.assertTrue(numpy.all(val.todense() == (b.multiply(a))))
-                self.assertTrue(numpy.all(val.todense() == numpy.array(
-                    [[1, 0], [9, 0], [0, 36]])))
+            for a in [numpy.array(array1), tensor.as_tensor_variable(array1)]:
+                b = mtype(array2)
+                bR = as_sparse_variable(b)
+                self.assertFalse(bR.data is b)  # constants are copied
+                self.assertTrue(_is_sparse(b))
+                self.assertTrue(_is_sparse_variable(bR))
+
+                apb = op(a, bR)
+
+                self.assertTrue(apb.type.dtype == a.dtype, apb.type.dtype)
+                self.assertTrue(apb.type.dtype == bR.type.dtype, apb.type.dtype)
+
+                val = eval_outputs([apb])
+                self.assertTrue(val.shape == (3, 2))
+                if op is add:
+                    self.assertTrue(_is_dense_variable(apb))
+                    self.assertTrue(numpy.all(val == (array1 + b)))
+                    ans = numpy.array([[1., 2], [3, 4], [5, 6]])
+                    self.assertTrue(numpy.all(val == ans))
+                elif op is mul:
+                    self.assertTrue(_is_sparse_variable(apb))
+                    self.assertTrue(numpy.all(val.todense() == (b.multiply(array1))))
+                    self.assertTrue(numpy.all(val.todense() == numpy.array(
+                        [[1, 0], [9, 0], [0, 36]])))
 
     def _testDS(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]),
                 array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])):
         for mtype in _mtypes:
-            a = mtype(array1)
-            aR = as_sparse_variable(a)
-            self.assertFalse(aR.data is a)
-            self.assertTrue(_is_sparse(a))
-            self.assertTrue(_is_sparse_variable(aR))
-
-            b = numpy.asarray(array2)
-            bR = tensor.as_tensor_variable(b)
-            self.assertFalse(bR.data is b)
-            self.assertTrue(_is_dense(b))
-            self.assertTrue(_is_dense_variable(bR))
-
-            apb = op(aR, bR)
-
-            self.assertTrue(apb.type.dtype == aR.type.dtype, apb.type.dtype)
-            self.assertTrue(apb.type.dtype == bR.type.dtype, apb.type.dtype)
-
-            val = eval_outputs([apb])
-            self.assertTrue(val.shape == (3, 2))
-            if op is add:
-                self.assertTrue(_is_dense_variable(apb))
-                self.assertTrue(numpy.all(val == (a + b)))
-                ans = numpy.array([[1., 2], [3, 4], [5, 6]])
-                self.assertTrue(numpy.all(val == ans))
-            elif op is mul:
-                self.assertTrue(_is_sparse_variable(apb))
-                ans = numpy.array([[1, 0], [9, 0], [0, 36]])
-                self.assertTrue(numpy.all(val.todense() == (a.multiply(b))))
-                self.assertTrue(numpy.all(val.todense() == ans))
+            for b in [numpy.asarray(array2), tensor.as_tensor_variable(array2)]:
+                a = mtype(array1)
+                aR = as_sparse_variable(a)
+                self.assertFalse(aR.data is a)
+                self.assertTrue(_is_sparse(a))
+                self.assertTrue(_is_sparse_variable(aR))
+
+                apb = op(aR, b)
+
+                self.assertTrue(apb.type.dtype == aR.type.dtype, apb.type.dtype)
+                self.assertTrue(apb.type.dtype == b.dtype, apb.type.dtype)
+
+                val = eval_outputs([apb])
+                self.assertTrue(val.shape == (3, 2))
+                if op is add:
+                    self.assertTrue(_is_dense_variable(apb))
+                    self.assertTrue(numpy.all(val == (a + array2)))
+                    ans = numpy.array([[1., 2], [3, 4], [5, 6]])
+                    self.assertTrue(numpy.all(val == ans))
+                elif op is mul:
+                    self.assertTrue(_is_sparse_variable(apb))
+                    ans = numpy.array([[1, 0], [9, 0], [0, 36]])
+                    self.assertTrue(numpy.all(val.todense() == (a.multiply(array2))))
+                    self.assertTrue(numpy.all(val.todense() == ans))
 
     def test_upcast(self):
         array1 = numpy.array([[1, 0], [3, 0], [0, 6]], dtype='float32')
@@ -718,18 +708,25 @@ class T_conversion(unittest.TestCase):
             self.assertTrue(str(val.dtype) == 'float64')
             self.assertTrue(val.format == 'csr')
 
-    if 1:
-        def test2(self):
-            #call dense_from_sparse
-            for t in _mtypes:
-                s = t(scipy.sparse.identity(5))
-                d = dense_from_sparse(s)
-                # s should be copied into the graph as a constant
-                s[0, 0] = 3.0  # changes s, but not the copy
-                val = eval_outputs([d])
-                return
-                self.assertTrue(str(val.dtype) == s.dtype)
-                self.assertTrue(numpy.all(val[0] == [1, 0, 0, 0, 0]))
+    def test_dense_from_sparse(self):
+        #call dense_from_sparse
+        for t in _mtypes:
+            s = t(scipy.sparse.identity(5))
+            s = as_sparse_variable(s)
+            d = dense_from_sparse(s)
+            val = eval_outputs([d])
+            self.assertTrue(str(val.dtype) == s.dtype)
+            self.assertTrue(numpy.all(val[0] == [1, 0, 0, 0, 0]))
+
+    def test_todense(self):
+        #call sparse_var.todense()
+        for t in _mtypes:
+            s = t(scipy.sparse.identity(5))
+            s = as_sparse_variable(s)
+            d = s.toarray()
+            val = eval_outputs([d])
+            self.assertTrue(str(val.dtype) == s.dtype)
+            self.assertTrue(numpy.all(val[0] == [1, 0, 0, 0, 0]))
 
     @staticmethod
     def check_format_ndim(format, ndim):

theano/tensor/blas_c.py

@@ -252,6 +252,8 @@ class CGer(BaseBLAS, Ger):
 
     def c_code_cache_version(self):
         return (8, blas_header_version())
+cger_inplace = CGer(True)
+cger_no_inplace = CGer(False)
 
 
 @local_optimizer([ger, ger_destructive])
@@ -269,8 +271,8 @@ def use_c_ger(node):
 
 @local_optimizer([CGer(False)])
 def make_c_ger_destructive(node):
-    if node.op == CGer(False):
-        return [CGer(True)(*node.inputs)]
+    if node.op == cger_no_inplace:
+        return [cger_inplace(*node.inputs)]
 
 
 ####### ####### #######
@@ -579,6 +581,8 @@ class CGemv(BaseBLAS, Gemv):
 
     def c_code_cache_version(self):
         return (10, blas_header_version())
+cgemv_inplace = CGemv(inplace=True)
+cgemv_no_inplace = CGemv(inplace=False)
 
 
 @local_optimizer([gemv_inplace, gemv_no_inplace])
@@ -596,8 +600,8 @@ def use_c_gemv(node):
 
 @local_optimizer([CGemv(inplace=False)])
 def make_c_gemv_destructive(node):
-    if node.op == CGemv(inplace=False):
-        return [CGemv(inplace=True)(*node.inputs)]
+    if node.op == cgemv_no_inplace:
+        return [cgemv_inplace(*node.inputs)]
 
 
 ####### ####### #######

theano/tensor/elemwise.py

@@ -546,7 +546,7 @@ class Elemwise(Op):
                 args.append(DimShuffle(
                     input.type.broadcastable,
                     ['x'] * difference + range(length),
-                    inplace=True)(input))
+                    inplace=False)(input))
         inputs = args
 
         #HERE: all the broadcast dims have the same length now

theano/tensor/opt.py

@@ -47,29 +47,43 @@ theano.configparser.AddConfigVar('on_shape_error',
 # Utilities
 
 
-def out2in(*local_opts):
+def out2in(*local_opts, **kwargs):
     """WRITEME """
+    name = (kwargs and kwargs.pop('name', None))
     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)
+        if not name:
+            name = local_opts.__name__
+    ret = opt.TopoOptimizer(local_opts,
+                            order='out_to_in',
+                            failure_callback=TopoOptimizer.warn_inplace,
+                            **kwargs)
+    if name:
+        ret.__name__ = name
+    return ret
 
 
 def in2out(*local_opts, **kwargs):
     """WRITEME """
+    name = (kwargs and kwargs.pop('name', None))
     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)
+        if not name:
+            #import pdb;pdb.set_trace()
+            name = local_opts.__name__
+    ret = opt.TopoOptimizer(local_opts,
+                            order='in_to_out',
+                            failure_callback=TopoOptimizer.warn_inplace,
+                            **kwargs)
+    if name:
+        ret.__name__ = name
+    return ret
 
 
 def _fill_chain(new_out, orig_inputs):
@@ -1075,7 +1089,7 @@ class ShapeFeature(object):
         for r, s in izip(node.outputs, o_shapes):
             self.set_shape(r, s)
 
-    def on_change_input(self, fgraph, node, i, r, new_r):
+    def on_change_input(self, fgraph, node, i, r, new_r, reason):
         if new_r not in self.shape_of:
             # It happen that the fgraph didn't called on_import for some
             # new_r.  This happen when new_r don't have an
@@ -2102,6 +2116,14 @@ def local_IncSubtensor_serialize(node):
 
         #print incsub_inputs, [id(i.owner.inputs[0]) for i in incsub_inputs]
 
+# We register it in a TopoOptimizer inside the canonizer EQ optimizer.
+# Otherwise in some cases it was making the EQ optimizer use 45. In
+# the TopoOptimizer, the EQ only use 6 passes.
+compile.optdb.register('pre_local_IncSubtensor_serialize',
+                       in2out(local_IncSubtensor_serialize),
+                       #Just before canonizer
+                       0.99, 'fast_run')
+
 
 #after priority 50 Destructive inplace operations
 #gemm is the first one now, at priority 70
@@ -3717,7 +3739,8 @@ register_specialize(local_add_specialize)
 # mul_to_neg = out2in(gof.LocalOptGroup(local_mul_to_neg))
 
 mul_canonizer = in2out(gof.LocalOptGroup(local_mul_canonizer, local_fill_cut,
-                                         local_fill_sink))
+                                         local_fill_sink),
+                       name='mul_canonizer_groups')
 
 
 def check_for_x_over_absX(numerators, denominators):
@@ -3859,7 +3882,8 @@ def add_calculate(num, denum, aslist=False, out_type=None):
 
 local_add_canonizer = Canonizer(T.add, T.sub, T.neg, add_calculate)
 add_canonizer = in2out(gof.LocalOptGroup(local_add_canonizer, local_fill_cut,
-                                         local_fill_sink))
+                                         local_fill_sink),
+                       name='add_canonizer_group')
 
 
 register_canonicalize(local_add_canonizer, name='local_add_canonizer')

theano/tensor/tests/test_opt.py

@@ -124,13 +124,27 @@ class test_dimshuffle_lift(unittest.TestCase):
         x, y, z = inputs([False] * 1, [False] * 2, [False] * 3)
         e = x + y + z
         g = FunctionGraph([x, y, z], [e])
-        self.assertTrue(str(g) == ("[Elemwise{add,no_inplace}("
-            "InplaceDimShuffle{x,0,1}(Elemwise{add,no_inplace}"
-            "(InplaceDimShuffle{x,0}(x), y)), z)]"), str(g))
+
+        # It does not really matter if the DimShuffles are inplace
+        # or not.
+        init_str_g_inplace = (
+            "[Elemwise{add,no_inplace}(InplaceDimShuffle{x,0,1}"
+            "(Elemwise{add,no_inplace}(InplaceDimShuffle{x,0}(x), y)), z)]")
+        init_str_g_noinplace = (
+            "[Elemwise{add,no_inplace}(DimShuffle{x,0,1}"
+            "(Elemwise{add,no_inplace}(DimShuffle{x,0}(x), y)), z)]")
+        self.assertTrue(str(g) in (init_str_g_inplace, init_str_g_noinplace),
+                        str(g))
+
+        opt_str_g_inplace = (
+            "[Elemwise{add,no_inplace}(Elemwise{add,no_inplace}"
+            "(InplaceDimShuffle{x,x,0}(x), InplaceDimShuffle{x,0,1}(y)), z)]")
+        opt_str_g_noinplace = (
+            "[Elemwise{add,no_inplace}(Elemwise{add,no_inplace}"
+            "(DimShuffle{x,x,0}(x), DimShuffle{x,0,1}(y)), z)]")
         dimshuffle_lift.optimize(g)
-        self.assertTrue(str(g) == ("[Elemwise{add,no_inplace}(Elemwise"
-            "{add,no_inplace}(InplaceDimShuffle{x,x,0}(x), InplaceDimShuffle"
-            "{x,0,1}(y)), z)]"), str(g))
+        self.assertTrue(str(g) in (opt_str_g_inplace, opt_str_g_noinplace),
+                        str(g))
 
 
 def test_add_canonizer_problem0():