Merge pull request #555 from nouiz/mixed

doc/library/config.txt

@@ -512,3 +512,26 @@ import theano and print the config variable, as in:
         A. Elemwise{add_no_inplace}
               B. log_likelihood_v_given_h
               C. log_likelihood_h
+
+.. attribute:: config.time_seq_optimizer
+
+    Bool value, default: False
+
+    Should all SeqOptimizer object print the time taked by each of its
+    optimizer. Each SeqOptimizer print something like this:
+
+    SeqOptimizer gpu_opt  time 0.014s for 8/9 nodes before/after optimization
+    [(0.0004410743713378906, ('InputToGpuOptimizer',
+    'InputToGpuOptimizer')), (0.0013730525970458984,
+    ('gpu_cut_transfers', 'EquilibriumOptimizer')),
+    (0.012573957443237305, ('gpu_local_optimizations',
+    'EquilibriumOptimizer'))]
+
+    This print the name of the SeqOptimizer (gpu_opt), the number of
+    Apply node in the graph before (8) and after (9)
+    optimizations. Then a list of tuple with 1 tuple by optimization
+    in this SeqOptimizer. The first element of the tuple is the time
+    by this optimization and then it is a tuple with the name of the
+    optimization and this class. This list is sorted from the sub
+    optimization that take the most time to the optimization that take
+    the less time.

theano/sandbox/cuda/tests/test_nnet.py

@@ -193,7 +193,8 @@ def test_softmax_with_bias():
             if not catch:
                 raise
             assert (e.args[0] ==
-              'Cuda error: kSoftmax_node_0: invalid configuration argument.\n')
+              'Cuda error: kSoftmaxWithBias_node_0: invalid configuration argument.\n'
+            ), e.args[0]
     cmp(2, 5)
     #we need to test n>32*1024 to check that we make the block loop.
     cmp(2 << 15, 5)

theano/sparse/tests/test_opt.py

@@ -20,10 +20,12 @@ def test_local_csm_properties_csm():
     data = tensor.vector()
     indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
                               tensor.ivector())
-
+    mode = theano.compile.mode.get_default_mode()
+    mode = mode.including("specialize", "local_csm_properties_csm")
     for CS, cast in [(CSC, sp.csc_matrix), (CSR, sp.csr_matrix)]:
         f = theano.function([data, indices, indptr, shape],
-                            csm_properties(CS(data, indices, indptr, shape)))
+                            csm_properties(CS(data, indices, indptr, shape)),
+                            mode=mode)
         #theano.printing.debugprint(f)
         assert not any(isinstance(node.op, (CSM, CSMProperties)) for node
                        in f.maker.env.toposort())