halfway through redoing env

gof/env.py

@@ -2,7 +2,7 @@
 from copy import copy
 
 import graph
-from features import Listener, Orderings, Constraint, Tool, uniq_features
+##from features import Listener, Orderings, Constraint, Tool, uniq_features
 import utils
 from utils import AbstractFunctionError
 
@@ -15,13 +15,6 @@ class InconsistencyError(Exception):
     pass
 
 
-def require_set(x):
-    try:
-        req = x.env_require
-    except AttributeError:
-        req = []
-    return req
-
 
 class Env(graph.Graph):
     """
@@ -35,10 +28,6 @@ class Env(graph.Graph):
     result in the subgraph by another, e.g. replace (x + x).out by (2
     * x).out. This is the basis for optimization in omega.
 
-    An Env's functionality can be extended with features, which must
-    be subclasses of L{Listener}, L{Constraint}, L{Orderings} or
-    L{Tool}.
-
     Regarding inputs and orphans:
 
     In the context of a computation graph, the inputs and orphans are
@@ -50,160 +39,151 @@ class Env(graph.Graph):
 
     ### Special ###
 
-    def __init__(self, inputs, outputs): #, consistency_check = True):
+    def __init__(self, inputs, outputs):
         """
         Create an Env which operates on the subgraph bound by the inputs and outputs
-        sets. If consistency_check is False, an illegal graph will be tolerated.
+        sets.
         """
 
-#         self._features = {}
-#         self._listeners = {}
-#         self._constraints = {}
-#         self._orderings = {}
-#         self._tools = {}
-
         self._features = []
         
-        # The inputs and outputs set bound the subgraph this Env operates on.
-        self.inputs = list(inputs)
-        self.outputs = list(outputs)
-
         # All nodes in the subgraph defined by inputs and outputs are cached in nodes
         self.nodes = set()
         
         # Ditto for results
-        self.results = set(self.inputs)
+        self.results = set()
 
-        # Set of all the results that are not an output of an op in the subgraph but
-        # are an input of an op in the subgraph.
-        # e.g. z for inputs=(x, y) and outputs=(x + (y - z),)
-        # We initialize them to the set of outputs; if an output depends on an input,
-        # it will be removed from the set of orphans.
-        self.orphans = set(outputs).difference(inputs)
+        self.inputs = list(inputs)
+        for input in self.inputs:
+            if input.owner is not None:
+                raise ValueError("One of the provided inputs is the output of an already existing node. " \
+                                 "If that is okay, either discard that input's owner or use graph.clone.")
+            self.__setup_r__(input)
 
-#         for feature_class in uniq_features(features):
-#             self.add_feature(feature_class, False)
+        self.outputs = outputs
+        self.__import_r__(outputs)
             
-        # Maps results to nodes that use them:
-        # if op.inputs[i] == v then (op, i) in self._clients[v]
-        self._clients = {}
         
         # List of functions that undo the replace operations performed.
         # e.g. to recover the initial graph one could write: for u in self.history.__reversed__(): u()
         self.history = []
 
-        self.__import_r__(self.outputs)
-#         for op in self.nodes():
-#             self.satisfy(op)
 
-#         if consistency_check:
-#             self.validate()
+    ### Setup a Result ###
 
+    def __setup_r__(self, r):
+        if hasattr(r, 'env') and r.env is not None and r.env is not self:
+            raise Exception("%s is already owned by another env" % r)
+        r.env = self
+        r.clients = []
 
-    ### Public interface ###
+    def __setup_node__(self, node):
+        if hasattr(node, 'env') and node.env is not self:
+            raise Exception("%s is already owned by another env" % node)
+        node.env = self
+        node.deps = {}
 
-    def add_output(self, output):
-        "Add an output to the Env."
-        self.outputs.add(output)
-        self.orphans.add(output)
-        self.__import_r__([output])
+
+    ### clients ###
 
     def clients(self, r):
         "Set of all the (op, i) pairs such that op.inputs[i] is r."
-        return self._clients.get(r, set())
+        return r.clients
 
-    def checkpoint(self):
+    def __add_clients__(self, r, all):
         """
-        Returns an object that can be passed to self.revert in order to backtrack
-        to a previous state.
+        r -> result
+        all -> list of (op, i) pairs representing who r is an input of.
+
+        Updates the list of clients of r with all.
         """
-        return len(self.history)
+        r.clients += all
 
-    def consistent(self):
+    def __remove_clients__(self, r, all):
         """
-        Returns True iff the subgraph is consistent and does not violate the
-        constraints set by the listeners.
+        r -> result
+        all -> list of (op, i) pairs representing who r is an input of.
+
+        Removes all from the clients list of r.
         """
-        try:
-            self.validate()
-        except InconsistencyError:
-            return False
-        return True
+        for entry in all:
+            r.clients.remove(entry)
+            # remove from orphans?
 
-#     def satisfy(self, x):
-#         "Adds the features required by x unless they are already present."
-#         for feature_class in require_set(x):
-#             self.add_feature(feature_class)
 
-    def extend(self, feature, do_import = True, validate = False):
-        """
-        @todo out of date
-        Adds an instance of the feature_class to this env's supported
-        features. If do_import is True and feature_class is a subclass
-        of Listener, its on_import method will be called on all the Nodes
-        already in the env.
-        """
-        if feature in self._features:
-            return # the feature is already present
-        self.__add_feature__(feature, do_import)
-        if validate:
-            self.validate()
+    ### import ###
 
-    def execute_callbacks(self, name, *args):
-        for feature in self._features:
-            try:
-                fn = getattr(feature, name)
-            except AttributeError:
-                continue
-            fn(*args)
+    def __import_r__(self, results):
+        # Imports the owners of the results
+        for node in set(r.owner for r in results if r is not None):
+            self.__import__(node)
 
-    def __add_feature__(self, feature, do_import):
-        self._features.append(feature)
-        publish = getattr(feature, 'publish', None)
-        if publish is not None:
-            publish()
-        if do_import:
-            try:
-                fn = feature.on_import
-            except AttributeError:
-                return
-            for node in self.io_toposort():
-                fn(node)
+    def __import__(self, node, check = True):
+        # We import the nodes in topological order. We only are interested
+        # in new nodes, so we use all results we know of as if they were the input set.
+        # (the functions in the graph module only use the input set to
+        # know where to stop going down)
+        new_nodes = graph.io_toposort(self.results, node.outputs)
+
+        if check:
+            for node in new_nodes:
+                if hasattr(node, 'env') and node.env is not self or \
+                       any(hasattr(r, 'env') and r.env is not self or \
+                           r.owner is None and not isinstance(r, Value) and r not in self.inputs
+                           for r in node.inputs + node.outputs):
+                    raise Exception("Could not import %s" % node)
+        
+        for node in new_nodes:
+            self.__setup_node__(node)
+            self.nodes.add(node)
+            for output in node.outputs:
+                self.__setup_r__(output)
+                self.results.add(output)
+            for i, input in enumerate(node.inputs):
+                if input not in self.results:
+                    if not isinstance(input, Value):
+                        raise TypeError("The graph to import contains a leaf that is not an input and has no default value " \
+                                        "(graph state is bad now - use check = True)", input)
+                    self.__setup_r__(input)
+                    self.results.add(input)
+                self.__add_clients__(input, [(node, i)])
+            assert node.env is self
+            self.execute_callbacks('on_import', node)
 
-    def __del_feature__(self, feature):
-        try:
-            del self._features[feature]
-        except:
-            pass
-        unpublish = hasattr(feature, 'unpublish')
-        if unpublish is not None:
-            unpublish()            
 
-    def get_feature(self, feature):
-        idx = self._features.index(feature)
-        return self._features[idx]
+    ### prune ###
 
-    def has_feature(self, feature):
-        return feature in self._features
+    def __prune_r__(self, results):
+        # Prunes the owners of the results.
+        for node in set(r.owner for r in results if r is not None):
+            self.__prune__(node)
+        for r in results:
+            if not r.clients and r in self.results:
+                self.results.remove(r)
+
+    def __prune__(self, node):
+        if node not in self.nodes:
+            raise Exception("%s does not belong to this Env and cannot be pruned." % node)
+        assert node.env is self
+        # If node's outputs have no clients, removes it from the graph
+        # and recursively tries to prune its inputs. If at least one
+        # of the op's outputs is an output to the graph or has a client
+        # then __prune__ is a no-op.
+        for output in node.outputs:
+            # Cannot prune an op which is an output or used somewhere
+            if self.clients(output) or output in self.outputs: #output in self.outputs or self.clients(output):
+                return
+        self.nodes.remove(node)
+        self.results.difference_update(node.outputs)
+        self.execute_callbacks('on_prune', node)
         
-    def nclients(self, r):
-        "Same as len(self.clients(r))."
-        return len(self.clients(r))
+        for i, input in enumerate(node.inputs):
+            self.__remove_clients__(input, [(node, i)])
+        self.__prune_r__(node.inputs)
 
-    def edge(self, r):
-        return r in self.inputs or r in self.orphans
 
-    def follow(self, r):
-        node = r.owner
-        if self.edge(r):
-            return None
-        else:
-            if node is None:
-                raise Exception("what the fuck")
-            return node.inputs
 
-    def has_node(self, node):
-        return node in self.nodes
+    ### replace ###
 
     def replace(self, r, new_r, consistency_check = True):
         """
@@ -222,6 +202,8 @@ class Env(graph.Graph):
         even if there is an inconsistency, unless the replacement
         violates hard constraints on the types involved.
         """
+        if r.env is not self:
+            raise Exception("Cannot replace %s because it does not belong to this Env" % r)
         assert r in self.results
 
         # Save where we are so we can backtrack
@@ -290,6 +272,108 @@ class Env(graph.Graph):
             self.revert(chk)
             raise
 
+
+
+
+
+    def checkpoint(self):
+        """
+        Returns an object that can be passed to self.revert in order to backtrack
+        to a previous state.
+        """
+        return len(self.history)
+
+    def consistent(self):
+        """
+        Returns True iff the subgraph is consistent and does not violate the
+        constraints set by the listeners.
+        """
+        try:
+            self.validate()
+        except InconsistencyError:
+            return False
+        return True
+
+    def extend(self, feature, do_import = True, validate = False):
+        """
+        @todo out of date
+        Adds an instance of the feature_class to this env's supported
+        features. If do_import is True and feature_class is a subclass
+        of Listener, its on_import method will be called on all the Nodes
+        already in the env.
+        """
+        if feature in self._features:
+            return # the feature is already present
+        self.__add_feature__(feature, do_import)
+        if validate:
+            self.validate()
+
+    def execute_callbacks(self, name, *args):
+        for feature in self._features:
+            try:
+                fn = getattr(feature, name)
+            except AttributeError:
+                continue
+            fn(*args)
+
+    def collect_callbacks(self, name, *args):
+        d = {}
+        for feature in self._features:
+            try:
+                fn = getattr(feature, name)
+            except AttributeError:
+                continue
+            d[feature] = fn(*args)
+        return d
+
+    def __add_feature__(self, feature, do_import):
+        self._features.append(feature)
+        publish = getattr(feature, 'publish', None)
+        if publish is not None:
+            publish()
+        if do_import:
+            try:
+                fn = feature.on_import
+            except AttributeError:
+                return
+            for node in self.io_toposort():
+                fn(node)
+
+    def __del_feature__(self, feature):
+        try:
+            del self._features[feature]
+        except:
+            pass
+        unpublish = hasattr(feature, 'unpublish')
+        if unpublish is not None:
+            unpublish()            
+
+    def get_feature(self, feature):
+        idx = self._features.index(feature)
+        return self._features[idx]
+
+    def has_feature(self, feature):
+        return feature in self._features
+
+    def nclients(self, r):
+        "Same as len(self.clients(r))."
+        return len(self.clients(r))
+
+    def edge(self, r):
+        return r in self.inputs or r in self.orphans
+
+    def follow(self, r):
+        node = r.owner
+        if self.edge(r):
+            return None
+        else:
+            if node is None:
+                raise Exception("what the fuck")
+            return node.inputs
+
+    def has_node(self, node):
+        return node in self.nodes
+
     def revert(self, checkpoint):
         """
         Reverts the graph to whatever it was at the provided
@@ -336,113 +420,6 @@ class Env(graph.Graph):
 
     ### Private interface ###
 
-    def __add_clients__(self, r, all):
-        """
-        r -> result
-        all -> list of (op, i) pairs representing who r is an input of.
-
-        Updates the list of clients of r with all.
-        """
-        self._clients.setdefault(r, set()).update(all)
-
-    def __remove_clients__(self, r, all):
-        """
-        r -> result
-        all -> list of (op, i) pairs representing who r is an input of.
-
-        Removes all from the clients list of r.
-        """
-        if not all:
-            return
-        self._clients[r].difference_update(all)
-        if not self._clients[r]:
-            del self._clients[r]
-            if r in self.orphans:
-                self.orphans.remove(r)
-
-    def __import_r_satisfy__(self, results):
-        # Satisfies the owners of the results.
-        for op in graph.ops(self.results, results):
-            self.satisfy(op)
-
-    def __import_r__(self, results):
-        # Imports the owners of the results
-        for result in results:
-            owner = result.owner
-            if owner:
-                self.__import__(result.owner)
-            if result not in self.results:
-                self.results.add(result)
-                self.orphans.add(result)
-
-    def __import__(self, op):
-        # We import the nodes in topological order. We only are interested
-        # in new nodes, so we use all results we know of as if they were the input set.
-        # (the functions in the graph module only use the input set to
-        # know where to stop going down)
-        new_nodes = graph.io_toposort(self.results.difference(self.orphans), op.outputs)
-        
-        for op in new_nodes:
-
-            self.nodes.add(op)
-            self.results.update(op.outputs)
-            self.orphans.difference_update(op.outputs)
-            
-            for i, input in enumerate(op.inputs):
-                self.__add_clients__(input, [(op, i)])
-                if input not in self.results:
-                    # This input is an orphan because if the op that
-                    # produced it was in the subgraph, io_toposort
-                    # would have placed it before, so we would have
-                    # seen it (or it would already be in the graph)
-                    self.orphans.add(input)
-                    self.results.add(input)
-            
-            self.execute_callbacks('on_import', op)
-#             for listener in self._listeners.values():
-#                 try:
-#                     listener.on_import(op)
-#                 except AbstractFunctionError:
-#                     pass
-    __import__.E_output = 'op output in Env.inputs'
-
-    def __prune_r__(self, results):
-        # Prunes the owners of the results.
-        for result in set(results):
-            if result in self.inputs:
-                continue
-            owner = result.owner
-            if owner:
-                self.__prune__(owner)
-#             if result in self.results:
-#                 self.results.remove(result)
-#                 if result in self.orphans:
-#                     self.orphans.remove(result)
-
-    def __prune__(self, op):
-        # If op's outputs have no clients, removes it from the graph
-        # and recursively tries to prune its inputs. If at least one
-        # of the op's outputs is an output to the graph or has a client
-        # then __prune__ is a no-op.
-        for output in op.outputs:
-            # Cannot prune an op which is an output or used somewhere
-            if self.clients(output) or output in self.outputs: #output in self.outputs or self.clients(output):
-                return
-        if op not in self.nodes: # this can happen from replacing an orphan
-            return
-        self.nodes.remove(op)
-        self.results.difference_update(op.outputs)
-        self.execute_callbacks('on_prune', op)
-#         for listener in self._listeners.values():
-#             try:
-#                 listener.on_prune(op)
-#             except AbstractFunctionError:
-#                 pass
-            
-        for i, input in enumerate(op.inputs):
-            self.__remove_clients__(input, [(op, i)])
-        self.__prune_r__(op.inputs)
-
     def __move_clients__(self, clients, r, new_r):
 
         if not (r.type == new_r.type):

gof/graph.py

@@ -6,11 +6,11 @@ from utils import object2
         
 
 def deprecated(f):
-    printme = True
+    printme = [True]
     def g(*args, **kwargs):
-        if printme:
+        if printme[0]:
             print 'gof.graph.%s deprecated: April 29' % f.__name__
-            printme = False
+            printme[0] = False
         return f(*args, **kwargs)
     return g
 
@@ -28,8 +28,6 @@ class Apply(object2):
         for input in inputs:
             if isinstance(input, Result):
                 self.inputs.append(input)
-#             elif isinstance(input, Type):
-#                 self.inputs.append(Result(input, None, None))
             else:
                 raise TypeError("The 'inputs' argument to Apply must contain Result instances, not %s" % input)
         self.outputs = []
@@ -42,12 +40,10 @@ class Apply(object2):
                 elif output.owner is not self or output.index != i:
                     raise ValueError("All output results passed to Apply must belong to it.")
                 self.outputs.append(output)
-#             elif isinstance(output, Type):
-#                 self.outputs.append(Result(output, self, i))
             else:
                 raise TypeError("The 'outputs' argument to Apply must contain Result instances with no owner, not %s" % output)
+    @deprecated
     def default_output(self):
-        print 'default_output deprecated: April 29'
         """
         Returns the default output for this Node, typically self.outputs[0].
         Depends on the value of node.op.default_output
@@ -66,6 +62,22 @@ class Apply(object2):
         return str(self)
     def __asapply__(self):
         return self
+    def clone(self):
+        return self.__class__(self.op, self.inputs, [output.clone() for output in self.outputs])
+    def clone_with_new_inputs(self, inputs, check_type = True):
+        if check_type:
+            for curr, new in zip(self.inputs, inputs):
+                if not curr.type == new.type:
+                    raise TypeError("Cannot change the type of this input.", curr, new)
+        new_node = self.clone()
+        new_node.inputs = inputs
+#         new_node.outputs = []
+#         for output in self.outputs:
+#             new_output = copy(output)
+#             new_output.owner = new_node
+#             new_node.outputs.append(new_output)
+        return new_node
+
     nin = property(lambda self: len(self.inputs))
     nout = property(lambda self: len(self.outputs))
 
@@ -77,7 +89,6 @@ class Result(object2):
         self.owner = owner
         self.index = index
         self.name = name
-
     def __str__(self):
         if self.name is not None:
             return self.name
@@ -88,22 +99,35 @@ class Result(object2):
             else:
                 return str(self.owner.op) + "." + str(self.index)
         else:
-            return "?::" + str(self.type)
+            return "<?>::" + str(self.type)
     def __repr__(self):
         return str(self)
     @deprecated
     def __asresult__(self):
         return self
+    def clone(self):
+        return self.__class__(self.type, None, None, self.name)
 
-class Constant(Result):
+class Value(Result):
     #__slots__ = ['data']
     def __init__(self, type, data, name = None):
         Result.__init__(self, type, None, None, name)
         self.data = type.filter(data)
-        self.indestructible = True
+    def __str__(self):
+        if self.name is not None:
+            return self.name
+        return "<" + str(self.data) + ">" #+ "::" + str(self.type)
+    def clone(self):
+        return self.__class__(self.type, self.data)
+
+class Constant(Value):
+    #__slots__ = ['data']
+    def __init__(self, type, data, name = None):
+        Value.__init__(self, type, data, name)
+###        self.indestructible = True
     def equals(self, other):
         # this does what __eq__ should do, but Result and Apply should always be hashable by id
-        return isinstance(other, Constant) and self.signature() == other.signature()
+        return type(other) == type(self) and self.signature() == other.signature()
     def signature(self):
         return (self.type, self.data)
     def __str__(self):
@@ -314,23 +338,12 @@ def clone_get_equiv(i, o, copy_inputs_and_orphans = False):
         if node is None: # result is an orphan
             if copy_inputs_and_orphans:
                 cpy = copy(result)
-                cpy.owner = None
-                cpy.index = None
                 d[result] = cpy
             else:
                 d[result] = result
             return d[result]
         else:
-            new_node = copy(node)
-            new_node.inputs = [clone_helper(input) for input in node.inputs]
-            new_node.outputs = []
-            for output in node.outputs:
-                new_output = copy(output)
-                new_output.owner = new_node
-                new_node.outputs.append(new_output)
-#             new_node = Apply(node.op,
-#                              [clone_helper(input) for input in node.inputs],
-#                              [output.type for output in node.outputs])
+            new_node = node.clone_with_new_inputs([clone_helper(input) for input in node.inputs])
             d[node] = new_node
             for output, new_output in zip(node.outputs, new_node.outputs):
                 d[output] = new_output