Merge pull request #3379 from lamblin/fix_pooldesc_merge

theano/gof/opt.py

@@ -484,9 +484,11 @@ class MergeFeature(object):
         # signature -> variable (for constants)
         self.const_sig_inv = _metadict()
 
-        # For all variables
+        # For all Apply nodes
         # Set of distinct (not mergeable) nodes
         self.nodes_seen = set()
+        # Ordered set of distinct (not mergeable) nodes without any input
+        self.noinput_nodes = OrderedSet()
 
         # Each element of scheduled is a list of list of (out, new_out) pairs.
         # Each list of pairs represent the substitution needed to replace all
@@ -514,6 +516,10 @@ class MergeFeature(object):
             self.nodes_seen.discard(node)
             self.process_node(fgraph, node)
 
+        # Since we are in on_change_input, node should have inputs.
+        if not isinstance(node, string_types):
+            assert node.inputs
+
         if isinstance(new_r, graph.Constant):
             self.process_constant(fgraph, new_r)
 
@@ -526,6 +532,8 @@ class MergeFeature(object):
 
     def on_prune(self, fgraph, node, reason):
         self.nodes_seen.discard(node)
+        if not node.inputs:
+            self.noinput_nodes.discard(node)
         for c in node.inputs:
             if isinstance(c, graph.Constant) and (len(c.clients) <= 1):
                 # This was the last node using this constant
@@ -592,7 +600,10 @@ class MergeFeature(object):
 
                     merge_candidates.extend(assert_clients)
         else:
-            merge_candidates = []
+            # If two nodes have no input, but perform the same operation,
+            # they are not always constant-folded, so we want to merge them.
+            # In that case, the candidates are all the nodes without inputs.
+            merge_candidates = self.noinput_nodes
 
         replacement_candidates = []
         for candidate in merge_candidates:
@@ -672,6 +683,8 @@ class MergeFeature(object):
             self.scheduled.append(replacement_candidates)
         else:
             self.nodes_seen.add(node)
+            if not node.inputs:
+                self.noinput_nodes.add(node)
 
     def get_merged_assert_input(self, node, candidate):
         new_inputs = []
@@ -2217,7 +2230,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
         process_count = {}
         for o in (opt.global_optimizers +
                   list(opt.get_local_optimizers()) +
-                  opt.final_optimizers):
+                  list(opt.final_optimizers)):
             process_count.setdefault(o, 0)
         for count in loop_process_count:
             for o, v in iteritems(count):
@@ -2246,7 +2259,8 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                     # Skip opt that have 0 times, they probably wasn't even tried.
                     print(blanc + "  ", '  %.3fs - %s' % (t, o), file=stream)
             print(file=stream)
-        gf_opts = [o for o in opt.global_optimizers + opt.final_optimizers
+        gf_opts = [o for o in (opt.global_optimizers +
+                               list(opt.final_optimizers))
                    if o.print_profile.func_code is not
                    Optimizer.print_profile.func_code]
         if not gf_opts:

theano/gof/tests/test_opt.py

@@ -3,7 +3,7 @@ from theano.gof.type import Type
 from theano.gof.graph import Variable, Apply, Constant
 from theano.gof.op import Op
 from theano.gof.opt import *  # noqa
-from theano.gof.fg import FunctionGraph as Env
+from theano.gof.fg import FunctionGraph
 from theano.gof.toolbox import *  # noqa
 
 from theano import tensor as T
@@ -100,7 +100,7 @@ class TestPatternOptimizer:
         # replacing the whole graph
         x, y, z = inputs()
         e = op1(op2(x, y), z)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, (op2, '1', '2'), '3'),
                          (op4, '3', '2')).optimize(g)
         assert str(g) == "[Op4(z, y)]"
@@ -108,7 +108,7 @@ class TestPatternOptimizer:
     def test_nested_out_pattern(self):
         x, y, z = inputs()
         e = op1(x, y)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, '1', '2'),
                          (op4, (op1, '1'), (op2, '2'), (op3, '1', '2'))).optimize(g)
         assert str(g) == "[Op4(Op1(x), Op2(y), Op3(x, y))]"
@@ -116,7 +116,7 @@ class TestPatternOptimizer:
     def test_unification_1(self):
         x, y, z = inputs()
         e = op1(op2(x, x), z)  # the arguments to op2 are the same
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, (op2, '1', '1'), '2'),  # they are the same in the pattern
                          (op4, '2', '1')).optimize(g)
         # So the replacement should occur
@@ -125,7 +125,7 @@ class TestPatternOptimizer:
     def test_unification_2(self):
         x, y, z = inputs()
         e = op1(op2(x, y), z)  # the arguments to op2 are different
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, (op2, '1', '1'), '2'),  # they are the same in the pattern
                          (op4, '2', '1')).optimize(g)
         # The replacement should NOT occur
@@ -135,7 +135,7 @@ class TestPatternOptimizer:
         # replacing inside the graph
         x, y, z = inputs()
         e = op1(op2(x, y), z)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op2, '1', '2'),
                          (op1, '2', '1')).optimize(g)
         assert str(g) == "[Op1(Op1(y, x), z)]"
@@ -146,7 +146,7 @@ class TestPatternOptimizer:
         # it should do the replacement and stop
         x, y, z = inputs()
         e = op1(op2(x, y), z)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op2, '1', '2'),
                          (op2, '2', '1'), ign=True).optimize(g)
         assert str(g) == "[Op1(Op2(y, x), z)]"
@@ -155,7 +155,7 @@ class TestPatternOptimizer:
         # it should replace all occurrences of the pattern
         x, y, z = inputs()
         e = op1(op2(x, y), op2(x, y), op2(y, z))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op2, '1', '2'),
                          (op4, '1')).optimize(g)
         assert str(g) == "[Op1(Op4(x), Op4(x), Op4(y))]"
@@ -165,7 +165,7 @@ class TestPatternOptimizer:
         # should work
         x, y, z = inputs()
         e = op1(op1(op1(op1(x))))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, (op1, '1')),
                          '1').optimize(g)
         assert str(g) == "[x]"
@@ -173,7 +173,7 @@ class TestPatternOptimizer:
     def test_nested_odd(self):
         x, y, z = inputs()
         e = op1(op1(op1(op1(op1(x)))))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, (op1, '1')),
                          '1').optimize(g)
         assert str(g) == "[Op1(x)]"
@@ -181,7 +181,7 @@ class TestPatternOptimizer:
     def test_expand(self):
         x, y, z = inputs()
         e = op1(op1(op1(x)))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, '1'),
                          (op2, (op1, '1')), ign=True).optimize(g)
         assert str(g) == "[Op2(Op1(Op2(Op1(Op2(Op1(x))))))]"
@@ -192,7 +192,7 @@ class TestPatternOptimizer:
         # = True or with other NavigatorOptimizers may differ.
         x, y, z = inputs()
         e = op1(op1(op1(op1(op1(x)))))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         TopoPatternOptimizer((op1, (op1, '1')),
                              (op1, '1'), ign=False).optimize(g)
         assert str(g) == "[Op1(x)]"
@@ -202,7 +202,7 @@ class TestPatternOptimizer:
         y = MyVariable('y')
         z = Constant(MyType(), 2, name='z')
         e = op1(op1(x, y), y)
-        g = Env([y], [e])
+        g = FunctionGraph([y], [e])
         PatternOptimizer((op1, z, '1'),
                          (op2, '1', z)).optimize(g)
         assert str(g) == "[Op1(Op2(y, z), y)]"
@@ -210,7 +210,7 @@ class TestPatternOptimizer:
     def test_constraints(self):
         x, y, z = inputs()
         e = op4(op1(op2(x, y)), op1(op1(x, y)))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
 
         def constraint(r):
             # Only replacing if the input is an instance of Op2
@@ -223,7 +223,7 @@ class TestPatternOptimizer:
     def test_match_same(self):
         x, y, z = inputs()
         e = op1(x, x)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, 'x', 'y'),
                          (op3, 'x', 'y')).optimize(g)
         assert str(g) == "[Op3(x, x)]"
@@ -231,7 +231,7 @@ class TestPatternOptimizer:
     def test_match_same_illegal(self):
         x, y, z = inputs()
         e = op2(op1(x, x), op1(x, y))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
 
         def constraint(r):
             # Only replacing if the input is an instance of Op2
@@ -245,7 +245,7 @@ class TestPatternOptimizer:
         x, y, z = inputs()
         e0 = op1(x, y)
         e = op3(op4(e0), e0)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op4, (op1, 'x', 'y')),
                          (op3, 'x', 'y')).optimize(g)
         assert str(g) == "[Op3(Op4(*1 -> Op1(x, y)), *1)]"
@@ -254,7 +254,7 @@ class TestPatternOptimizer:
         # replacing the whole graph
         x, y, z = inputs()
         e = op1(op_y(x, y), z)
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         PatternOptimizer((op1, (op_z, '1', '2'), '3'),
                          (op4, '3', '2')).optimize(g)
         str_g = str(g)
@@ -265,7 +265,7 @@ class TestPatternOptimizer:
 #         x, y, z = inputs()
 #         e0 = op1(x, y)
 #         e = op4(e0, e0)
-#         g = Env([x, y, z], [e])
+#         g = FunctionGraph([x, y, z], [e])
 #         PatternOptimizer((op4, (op1, 'x', 'y'), (op1, 'x', 'y')),
 #                          (op3, 'x', 'y')).optimize(g)
 #         assert str(g) == "[Op3(x, y)]"
@@ -280,24 +280,37 @@ class TestOpSubOptimizer:
     def test_straightforward(self):
         x, y, z = inputs()
         e = op1(op1(op1(op1(op1(x)))))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         OpSubOptimizer(op1, op2).optimize(g)
         assert str(g) == "[Op2(Op2(Op2(Op2(Op2(x)))))]"
 
     def test_straightforward_2(self):
         x, y, z = inputs()
         e = op1(op2(x), op3(y), op4(z))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         OpSubOptimizer(op3, op4).optimize(g)
         assert str(g) == "[Op1(Op2(x), Op4(y), Op4(z))]"
 
 
+class NoInputOp(Op):
+    __props__ = ('param',)
+
+    def __init__(self, param):
+        self.param = param
+
+    def make_node(self):
+        return Apply(self, [], [MyType()()])
+
+    def perform(self, node, inputs, output_storage):
+        output_storage[0][0] = self.param
+
+
 class TestMergeOptimizer:
 
     def test_straightforward(self):
         x, y, z = inputs()
         e = op1(op2(x, y), op2(x, y), op2(x, z))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         MergeOptimizer().optimize(g)
         assert str(g) == "[Op1(*1 -> Op2(x, y), *1, Op2(x, z))]"
 
@@ -306,7 +319,7 @@ class TestMergeOptimizer:
         y = Constant(MyType(), 2, name='y')
         z = Constant(MyType(), 2, name='z')
         e = op1(op2(x, y), op2(x, y), op2(x, z))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         MergeOptimizer().optimize(g)
         strg = str(g)
         assert strg == "[Op1(*1 -> Op2(x, y), *1, *1)]" \
@@ -315,14 +328,14 @@ class TestMergeOptimizer:
     def test_deep_merge(self):
         x, y, z = inputs()
         e = op1(op3(op2(x, y), z), op4(op3(op2(x, y), z)))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         MergeOptimizer().optimize(g)
         assert str(g) == "[Op1(*1 -> Op3(Op2(x, y), z), Op4(*1))]"
 
     def test_no_merge(self):
         x, y, z = inputs()
         e = op1(op3(op2(x, y)), op3(op2(y, x)))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         MergeOptimizer().optimize(g)
         assert str(g) == "[Op1(Op3(Op2(x, y)), Op3(Op2(y, x)))]"
 
@@ -330,7 +343,7 @@ class TestMergeOptimizer:
         x, y, z = inputs()
         e1 = op3(op2(x, y))
         e2 = op3(op2(x, y))
-        g = Env([x, y, z], [e1, e2])
+        g = FunctionGraph([x, y, z], [e1, e2])
         MergeOptimizer().optimize(g)
         assert str(g) == "[*1 -> Op3(Op2(x, y)), *1]"
 
@@ -339,7 +352,7 @@ class TestMergeOptimizer:
         e1 = op1(x, y)
         e2 = op2(op3(x), y, z)
         e = op1(e1, op4(e2, e1), op1(e2))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         MergeOptimizer().optimize(g)
         strg = str(g)
         # note: graph.as_string can only produce the following two possibilities, but if
@@ -357,7 +370,7 @@ class TestMergeOptimizer:
             e1 = op1(y, z)
         finally:
             config.compute_test_value = ctv_backup
-        g = Env([x, y, z], [e1])
+        g = FunctionGraph([x, y, z], [e1])
         MergeOptimizer().optimize(g)
         strg = str(g)
         assert strg == '[Op1(y, y)]' or strg == '[Op1(z, z)]'
@@ -367,7 +380,7 @@ class TestMergeOptimizer:
         x1 = T.matrix('x1')
         x2 = T.matrix('x2')
         e = T.dot(x1, x2) + T.dot(T.opt.assert_op(x1, (x1 > x2).all()), x2)
-        g = Env([x1, x2], [e])
+        g = FunctionGraph([x1, x2], [e])
         MergeOptimizer().optimize(g)
         strg = theano.printing.debugprint(g, file='str')
         strref = '''Elemwise{add,no_inplace} [@A] ''   4
@@ -391,7 +404,7 @@ class TestMergeOptimizer:
         x3 = T.matrix('x3')
         e = T.dot(T.opt.assert_op(x1, (x1 > x3).all()), x2) +\
             T.dot(T.opt.assert_op(x1, (x1 > x2).all()), x2)
-        g = Env([x1, x2, x3], [e])
+        g = FunctionGraph([x1, x2, x3], [e])
         MergeOptimizer().optimize(g)
         strg = theano.printing.debugprint(g, file='str')
         strref1 = '''Elemwise{add,no_inplace} [@A] ''   6
@@ -434,7 +447,7 @@ class TestMergeOptimizer:
         x3 = T.matrix('x3')
         e = T.dot(T.opt.assert_op(x1, (x1 > x3).all()), x2) +\
             T.dot(x1, T.opt.assert_op(x2, (x2 > x3).all()))
-        g = Env([x1, x2, x3], [e])
+        g = FunctionGraph([x1, x2, x3], [e])
         MergeOptimizer().optimize(g)
         strg = theano.printing.debugprint(g, file='str')
         strref = '''Elemwise{add,no_inplace} [@A] ''   7
@@ -463,7 +476,7 @@ class TestMergeOptimizer:
         x3 = T.matrix('x3')
         e = T.dot(x1, T.opt.assert_op(x2, (x2 > x3).all())) +\
             T.dot(T.opt.assert_op(x1, (x1 > x3).all()), x2)
-        g = Env([x1, x2, x3], [e])
+        g = FunctionGraph([x1, x2, x3], [e])
         MergeOptimizer().optimize(g)
         strg = theano.printing.debugprint(g, file='str')
         strref = '''Elemwise{add,no_inplace} [@A] ''   7
@@ -485,13 +498,25 @@ class TestMergeOptimizer:
         print(strg)
         assert strg == strref, (strg, strref)
 
+    def test_merge_noinput(self):
+        # Check that identical Apply nodes without inputs will be merged
+        x = NoInputOp(param=0)()
+        y = NoInputOp(param=0)()
+        z = NoInputOp(param=1)()
+
+        fg = FunctionGraph([], [x, y, z])
+        MergeOptimizer().optimize(fg)
+        no_input_ops = [n for n in fg.apply_nodes
+                        if isinstance(n.op, NoInputOp)]
+        assert len(no_input_ops) == 2, fg.apply_nodes
+
 
 class TestEquilibrium(object):
 
     def test_1(self):
         x, y, z = map(MyVariable, 'xyz')
         e = op3(op4(x, y))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         # print g
         opt = EquilibriumOptimizer(
             [PatternSub((op1, 'x', 'y'), (op2, 'x', 'y')),
@@ -506,7 +531,7 @@ class TestEquilibrium(object):
     def test_2(self):
         x, y, z = map(MyVariable, 'xyz')
         e = op1(op1(op3(x, y)))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         # print g
         opt = EquilibriumOptimizer(
             [PatternSub((op1, (op2, 'x', 'y')), (op4, 'x', 'y')),
@@ -522,7 +547,7 @@ class TestEquilibrium(object):
     def test_low_use_ratio(self):
         x, y, z = map(MyVariable, 'xyz')
         e = op3(op4(x, y))
-        g = Env([x, y, z], [e])
+        g = FunctionGraph([x, y, z], [e])
         # print 'before', g
         # display pesky warnings along with stdout
         # also silence logger for 'theano.gof.opt'

theano/sandbox/cuda/tests/test_dnn.py

@@ -68,6 +68,19 @@ def test_dnn_conv_desc_merge():
         assert d1 == d2
 
 
+def test_dnn_pool_desc_merge():
+    if not cuda.dnn.dnn_available():
+        raise SkipTest(cuda.dnn.dnn_available.msg)
+
+    x = theano.tensor.ftensor4('x')
+    y = dnn.dnn_pool(x, (2, 2))
+    z = dnn.dnn_pool(x, (2, 2))
+    f = theano.function([x], [y, z])
+    descs = [n for n in f.maker.fgraph.apply_nodes
+             if isinstance(n.op, dnn.GpuDnnPoolDesc)]
+    assert len(descs) == 1, f.maker.fgraph
+
+
 def test_dnn_conv_merge():
     """This test that we merge correctly multiple dnn_conv.