Merge pull request #4128 from caglar/fix_extract_constant

theano/tensor/nnet/sigm.py

@@ -413,6 +413,7 @@ log1msigm_to_softplus = gof.PatternSub(
     values_eq_approx=values_eq_approx_remove_inf,
     skip_identities_fn=_skip_mul_1)
 
+
 log1pexp_to_softplus = gof.PatternSub(
     (tensor.log1p,
      (tensor.exp, 'x')),
@@ -420,12 +421,20 @@ log1pexp_to_softplus = gof.PatternSub(
     values_eq_approx=values_eq_approx_remove_inf,
     allow_multiple_clients=True)
 
+log1p_neg_sigmoid = gof.PatternSub(
+    (tensor.log1p,
+     (tensor.neg, (sigmoid, 'x'))),
+    (tensor.neg, (softplus, 'x')),
+    values_eq_approx=values_eq_approx_remove_inf,
+    allow_multiple_clients=True)
+
 opt.register_stabilize(logsigm_to_softplus, name='logsigm_to_softplus')
 opt.register_stabilize(log1msigm_to_softplus, name='log1msigm_to_softplus')
 opt.register_stabilize(log1pexp_to_softplus, name='log1pexp_to_softplus')
+opt.register_stabilize(log1p_neg_sigmoid, name='log1p_neg_sigmoid,')
 
 
-def is_1pexp(t):
+def is_1pexp(t, only_process_constants=True):
     """
 
     Returns
@@ -437,8 +446,9 @@ def is_1pexp(t):
     """
     if t.owner and t.owner.op == tensor.add:
         scalars, scalar_inputs, nonconsts = \
-            opt.scalarconsts_rest(t.owner.inputs)
-        # scalar_inputs are potentially dimshuffled and fill'd scalars
+            opt.scalarconsts_rest(t.owner.inputs,
+                                  only_process_constants=only_process_constants)
+        # scalar_inputs are potentially dimshuffled and filled with scalars
         if len(nonconsts) == 1:
             maybe_exp = nonconsts[0]
             if maybe_exp.owner and maybe_exp.owner.op == tensor.exp:
@@ -947,7 +957,7 @@ def local_inv_1_plus_exp(node):
         inv_arg = node.inputs[0]
         if inv_arg.owner and inv_arg.owner.op == tensor.add:
             scalars, scalar_inputs, nonconsts = \
-                opt.scalarconsts_rest(inv_arg.owner.inputs)
+                opt.scalarconsts_rest(inv_arg.owner.inputs, only_process_constants=True)
             # scalar_inputs are potentially dimshuffled and fill'd scalars
             if len(nonconsts) == 1:
                 if nonconsts[0].owner and nonconsts[0].owner.op == tensor.exp:

theano/tensor/nnet/tests/test_sigm.py

@@ -356,7 +356,6 @@ class T_sigmoid_opts(unittest.TestCase):
         f = theano.function([x], s, mode=mode)
         assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
         topo = f.maker.fgraph.toposort()
-        assert len(topo) > 1
         assert not any([n.op == sigmoid for n in topo])
         ux_v = f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
 
@@ -467,15 +466,17 @@ class T_sigmoid_utils(unittest.TestCase):
         try:
             x = tensor.vector('x')
             exp = tensor.exp
-            assert is_1pexp(1 + exp(x)) == (False, x)
-            assert is_1pexp(exp(x) + 1) == (False, x)
-            for neg, exp_arg in imap(is_1pexp, [(1 + exp(-x)), (exp(-x) + 1)]):
+            assert is_1pexp(1 + exp(x), False) == (False, x)
+            assert is_1pexp(exp(x) + 1, False) == (False, x)
+            for neg, exp_arg in imap(lambda x:
+                                     is_1pexp(x, only_process_constants=False),
+                                     [(1 + exp(-x)), (exp(-x) + 1)]):
                 assert not neg and theano.gof.graph.is_same_graph(exp_arg, -x)
-            assert is_1pexp(1 - exp(x)) is None
-            assert is_1pexp(2 + exp(x)) is None
-            assert is_1pexp(exp(x) + 2) is None
-            assert is_1pexp(exp(x) - 1) is None
-            assert is_1pexp(-1 + exp(x)) is None
-            assert is_1pexp(1 + 2 * exp(x)) is None
+            assert is_1pexp(1 - exp(x), False) is None
+            assert is_1pexp(2 + exp(x), False) is None
+            assert is_1pexp(exp(x) + 2, False) is None
+            assert is_1pexp(exp(x) - 1, False) is None
+            assert is_1pexp(-1 + exp(x), False) is None
+            assert is_1pexp(1 + 2 * exp(x), False) is None
         finally:
             config.warn.identify_1pexp_bug = backup

theano/tensor/tests/test_opt.py

@@ -1635,8 +1635,8 @@ def test_log_add():
 def test_local_useless_slice():
     # test a simple matrix
     x = tensor.matrix('x')
-    mode_unopt = compile.get_default_mode().excluding("local_useless_slice")
-    mode_opt = compile.get_default_mode().including("local_useless_slice")
+    mode_unopt = compile.get_default_mode().excluding("local_useless_slice", "local_mul_canonizer")
+    mode_opt = compile.get_default_mode().including("local_useless_slice").excluding("local_mul_canonizer")
 
     # test with and without the useless slice
     o = 2 * x[0, :]
@@ -2124,7 +2124,7 @@ class test_local_subtensor_lift(unittest.TestCase):
         f1 = function([x], newx[:2, :5], mode=mode_opt)
         # Check stacktrace was copied over correctly after opt was applied
         self.assertTrue(check_stack_trace(f1, ops_to_check=[
-                Subtensor, tensor.Rebroadcast]))
+                        Subtensor, tensor.Rebroadcast]))
         prog = f1.maker.fgraph.toposort()
         assert isinstance(prog[0].op, tensor.Subtensor)
         assert isinstance(prog[1].op, tensor.Rebroadcast)
@@ -2140,7 +2140,7 @@ class test_local_subtensor_lift(unittest.TestCase):
         f2 = function([y], newy[:, 3, 0, :], mode=mode_opt)
         # Check stacktrace was copied over correctly after opt was applied
         self.assertTrue(check_stack_trace(f2, ops_to_check=[
-                Subtensor, tensor.Rebroadcast]))
+                        Subtensor, tensor.Rebroadcast]))
         prog = f2.maker.fgraph.toposort()
         assert isinstance(prog[0].op, tensor.Subtensor)
         assert isinstance(prog[1].op, tensor.Rebroadcast)