Merge pull request #4554 from nouiz/scan_opt

theano/scan_module/scan_opt.py

@@ -202,7 +202,7 @@ def remove_constants_and_unused_inputs_scan(node):
         # DEBUG CHECK
         nwScan = scan_op.Scan(nw_inner, op_outs, nw_info)
         nw_outs = nwScan(*nw_outer, **dict(return_list=True))
-        return dict([("remove", [node])] + list(zip(node.outputs, nw_outs)))
+        return OrderedDict([("remove", [node])] + list(zip(node.outputs, nw_outs)))
     else:
         return False
 
@@ -2072,8 +2072,8 @@ def scan_merge_inouts(node):
             new_outer_out_mit_mot.append(outer_omm)
     na.outer_out_mit_mot = new_outer_out_mit_mot
     if remove:
-        return dict([("remove", remove)] +
-                    list(zip(node.outputs, na.outer_outputs)))
+        return OrderedDict([("remove", remove)] +
+                           list(zip(node.outputs, na.outer_outputs)))
     return na.outer_outputs
 
 

theano/tensor/blas.py

@@ -152,6 +152,7 @@ from theano.tensor import basic as T
 from theano.tensor.blas_headers import blas_header_text
 from theano.tensor.blas_headers import blas_header_version
 from theano.tensor.opt import in2out, local_dimshuffle_lift
+from theano.tensor.type import values_eq_approx_remove_inf_nan
 
 _logger = logging.getLogger('theano.tensor.blas')
 
@@ -1465,6 +1466,7 @@ class GemmOptimizer(Optimizer):
                 if new_outputs:
                     new_outputs, old_dot22 = new_outputs
                     assert len(new_outputs) == len(node.outputs)
+                    new_outputs[0].tag.values_eq_approx = values_eq_approx_remove_inf_nan
                     try:
                         fgraph.replace_all_validate_remove(
                             list(zip(node.outputs, new_outputs)),

theano/tensor/tests/test_blas_c.py

@@ -132,9 +132,11 @@ class TestCGemv(TestCase, TestOptimizationMixin):
         self.a = tensor.tensor(dtype=dtype, broadcastable=())
 
     def test_nan_beta_0(self):
+        mode = self.mode.including()
+        mode.check_isfinite = False
         f = theano.function([self.A, self.x, self.y, self.a],
                             self.a*self.y + theano.dot(self.A, self.x),
-                            mode=self.mode)
+                            mode=mode)
         Aval = numpy.ones((3, 1), dtype=self.dtype)
         xval = numpy.ones((1,), dtype=self.dtype)
         yval = float('NaN') * numpy.ones((3,), dtype=self.dtype)