changed from marking variables directly to making a dictionary mapping

theano/gradient.py

@@ -481,14 +481,11 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
     if not using_list and not using_tuple:
         wrt = [ wrt ]
 
-    #set of variables that has had children added to it
-    marked = set([])
+    #var_to_node_to_idx[var][node] = i means node has var as input at position i
+    var_to_node_to_idx = {}
     #set of variables that have been added to their parents
     accounted_for = set([])
 
-    #use a try/finally to make sure we don't leave any marks
-    #on the variables
-    try:
     #mark the variables in the relevant subgraph with
     #a dictionary called chidlren
     #var._children[node] gives the index of var in _children.inputs
@@ -499,11 +496,9 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
         if var.owner is not None:
             node = var.owner
             for i, ipt in enumerate(node.inputs):
-                    if not hasattr(ipt, '_children'):
-                        marked.add(ipt)
-                        ipt._children = {}
-                    if node not in ipt._children:
-                        ipt._children[node] = i
+                if ipt not in var_to_node_to_idx:
+                    var_to_node_to_idx[ipt] = {}
+                var_to_node_to_idx[ipt][node] = i
                 account_for(ipt)
 
     account_for(cost)
@@ -520,11 +515,11 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
         grad_dict[const] = tensor.zeros_like(const)
 
     #variables that do not influence the cost have zero gradient.
-        #if wrt is such a varibale, populate the grad_dict with this info
-        #so that wrt not having _children won't cause an error below
+    #if wrt is such a variable, populate the grad_dict with this info
+    #so that wrt not being in var_to_node_to_idx won't cause an error below
     #according to the flag, possibly raise an error if wrt is disconnected
     for elem in wrt:
-            if elem not in marked and elem is not cost:
+        if elem not in var_to_node_to_idx and elem is not cost:
             message = ("grad method was asked to compute the gradient "
                     "with respect to a variable that is not part of "
                     "the computational graph of the cost, or is used "
@@ -547,10 +542,14 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
     #populate term_dict[node] and return it
     def access_term_cache(node):
         if node not in term_dict:
+
+            inputs = node.inputs
+            output_grads = [ access_grad_cache(var) for var in node.outputs ]
+            input_grads = node.op.grad(inputs, output_grads)
+
             #must convert to list in case the op returns a tuple
             #we won't be able to post-process out the Nones if it does that
-                term_dict[node] = list(node.op.grad(node.inputs,
-                        [access_grad_cache(var) for var in node.outputs]))
+            term_dict[node] = list(input_grads)
             for i in xrange(len(term_dict[node])):
                 if term_dict[node][i] is None:
                     term_dict[node][i] = tensor.zeros_like(node.inputs[i])
@@ -569,11 +568,12 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
     #populate grad_dict[var] and return it
     def access_grad_cache(var):
         if var not in grad_dict:
-                if hasattr(var,'_children'):
+            if var in var_to_node_to_idx:
                 terms = []
-                    for child in var._children.keys():
-                        idx = var._children[child]
-                        term = access_term_cache(child)[idx]
+                node_to_idx = var_to_node_to_idx[var]
+                for node in node_to_idx:
+                    idx = node_to_idx[node]
+                    term = access_term_cache(node)[idx]
 
                     if isinstance(term.type,NaNType):
                         raise TypeError("tensor.grad encountered a NaN. "+\
@@ -591,10 +591,6 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
 
 
     rval = [ access_grad_cache(elem) for elem in wrt ]
-    finally:
-        #take the marks out
-        for node in marked:
-            del node._children
 
     if using_tuple:
         rval = tuple(rval)
@@ -614,17 +610,13 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
     wrt = graph_inputs
 
 
-    #set of variables that has had children added to it
-    marked = set([])
+    #var_to_node_to_idx[var][node] = i means node has var as input at position i
+    var_to_node_to_idx = {}
     #set of variables that have been added to their parents
     accounted_for = set([])
 
-    #use a try/finally to make sure we don't leave any marks
-    #on the variables
-    try:
-        #mark the variables in the relevant subgraph with
-        #a dictionary called chidlren
-        #var._children[node] gives the index of var in _children.inputs
+    #notify the parents the variables in the relevant subgraph
+    #that they have children
     def account_for(var):
         if var in accounted_for:
             return
@@ -632,11 +624,9 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
         if var.owner is not None:
             node = var.owner
             for i, ipt in enumerate(node.inputs):
-                    if not hasattr(ipt, '_children'):
-                        marked.add(ipt)
-                        ipt._children = {}
-                    if node not in ipt._children:
-                        ipt._children[node] = i
+                if ipt not in var_to_node_to_idx:
+                    var_to_node_to_idx[ipt] = {}
+                var_to_node_to_idx[ipt][node] = i
                 account_for(ipt)
 
     for output in outputs:
@@ -649,16 +639,15 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
         grad_dict[output] = output_grad
 
     #variables that do not influence the cost have zero gradient.
-        #if wrt is such a varibale, populate the grad_dict with this info
-        #so that wrt not having _children won't cause an error below
+    #if wrt is such a variable, populate the grad_dict with this info
+    #so that wrt not being in var_to_node_to_idx won't cause an error below
     #according to the flag, possibly raise an error if wrt is disconnected
     for elem in wrt:
-            if elem not in marked and elem not in outputs:
+        if elem not in var_to_node_to_idx and elem not in outputs:
             message = ("grad method was asked to compute the gradient "
                     "with respect to a variable that is not part of "
                     "the computational graph of the cost, or is used "
                     "only by a non-differentiable operator: %s" % elem)
-                #raise ValueError(message)
             grad_dict[elem] = elem.zeros_like()
 
     #build a dict mapping node to the terms node contributes to each of its inputs' gradients
@@ -689,11 +678,12 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
     #populate grad_dict[var] and return it
     def access_grad_cache(var):
         if var not in grad_dict:
-                if hasattr(var,'_children'):
+            if var in var_to_node_to_idx:
                 terms = []
-                    for child in var._children.keys():
-                        idx = var._children[child]
-                        term = access_term_cache(child)[idx]
+                node_to_idx = var_to_node_to_idx[var]
+                for node in node_to_idx:
+                    idx = node_to_idx[node]
+                    term = access_term_cache(node)[idx]
 
                     if isinstance(term.type,NaNType):
                         raise TypeError("tensor.grad encountered a NaN. "+\
@@ -709,10 +699,6 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
 
 
     rval = [ access_grad_cache(elem) for elem in wrt ]
-    finally:
-        #take the marks out
-        for node in marked:
-            del node._children
 
     return grad_dict
 
@@ -1149,6 +1135,7 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
         return plain
 
     t_r = shared(random_projection())
+    t_r.name = 'random_projection'
 
     # random projection of o onto t_r
     # This sum() is defined above, it's not the builtin sum.
@@ -1178,6 +1165,7 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
                 num_grad.max_err(analytic_grad, abs_tol, rel_tol)
 
         if max_abs_err > abs_tol and max_rel_err > rel_tol:
+
             raise verify_grad.E_grad(max_arg, max_err_pos,
                     max_abs_err, max_rel_err, abs_tol, rel_tol)