Cleaned up upcast check. Copied stack trace. Added tests in T_cast_cast of test_opt.py

theano/tensor/opt.py

@@ -2232,6 +2232,8 @@ def local_cast_cast(node):
         else:
             # Apply the second cast only
             v = node.op(base)
+            # Copy stack trace from the output of the original cast
+            copy_stack_trace(node.outputs[0], v)
             return [v]
 
 
@@ -2240,38 +2242,20 @@ def upcast(type1, type2):
     type2 from type1 constitutes an upcast.
 
     """
-    upcast_pairs = (
-        ('int8', 'int16'), ('int8', 'int32'), ('int8', 'int64'),
-        ('int16', 'int32'), ('int16', 'int64'),
-        ('int32', 'int64'),
-        ('uint8', 'uint16'), ('uint8', 'uint32'), ('uint8', 'uint64'),
-        ('uint16', 'uint32'), ('uint16', 'uint64'),
-        ('uint32', 'uint64'),
-        ('float16', 'float32'), ('float16', 'float32'), ('float16', 'float64'),
-        ('float32', 'float64'),
-        ('complex64', 'complex128'),
-
-        ('uint8', 'int16'), ('uint8', 'int32'), ('uint8', 'int64'),
-        ('uint16', 'int32'), ('uint16', 'int64'),
-        ('uint32', 'int64'),
-        ('int8', 'float16'), ('int8', 'float32'), ('int8', 'float64'),
-        ('int16', 'float32'), ('int16', 'float64'),
-        ('int32', 'float64'),
-        ('uint8', 'float16'), ('uint8', 'float32'), ('uint8', 'float64'),
-        ('uint16', 'float32'), ('uint16', 'float64'),
-        ('uint32', 'float64'),
-
-        ('int8', 'complex64'), ('int16', 'complex64'),
-        ('uint8', 'complex64'), ('uint16', 'complex64'),
-        ('float32', 'complex64'),
-        ('int8', 'complex128'), ('int16', 'complex128'), ('int32', 'complex128'),
-        ('uint8', 'complex128'), ('uint16', 'complex128'), ('uint32', 'complex128'),
-        ('float32', 'complex128'), ('float64', 'complex128')
-    )
+    category = {
+        # Pair of numbers : the 'super-index' and 'sub-index'
+        'uint8': (0, 0), 'uint16': (0, 1), 'uint32': (0, 2), 'uint64': (0, 3),
+        'int8': (1, 0), 'int16': (1, 1), 'int32': (1, 2), 'int64': (1, 3),
+        'float16': (2, 0.5), 'float32': (2, 1.5), 'float64': (2, 2.5),
+        'complex64': (3, 2), 'complex128': (3, 3)
+    }
 
-    for pair in upcast_pairs:
-        if(type1 == pair[0] and type2 == pair[1]):
+    cat1 = category[type1]
+    cat2 = category[type2]
+
+    if(cat2[0] >= cat1[0] and cat2[1] > cat1[1]):
         return True
+    else:
         return False
 
 

theano/tensor/tests/test_opt.py

@@ -4650,7 +4650,7 @@ class T_cast_cast(unittest.TestCase):
         mode = theano.compile.get_default_mode()
         self.mode = mode.including('local_cast_cast')
 
-    def test(self):
+    def test_consecutive(self):
         x = T.fmatrix()
         o = T.Elemwise(scal.Cast(scal.Scalar("float64")))(x.astype("float64"))
         f = theano.function([x], o, mode=self.mode)
@@ -4658,7 +4658,7 @@ class T_cast_cast(unittest.TestCase):
         f(dx)
         topo = f.maker.fgraph.toposort()
         assert len(topo) == 1
-        assert isinstance(topo[0].op, T.Elemwise)
+        assert isinstance(topo[0].op.scalar_op, scal.basic.Cast)
 
         x = T.dmatrix()
         o = T.Elemwise(scal.Cast(scal.Scalar("float32")))(x.astype("float32"))
@@ -4667,7 +4667,38 @@ class T_cast_cast(unittest.TestCase):
         f(dx)
         topo = f.maker.fgraph.toposort()
         assert len(topo) == 1
-        assert isinstance(topo[0].op, T.Elemwise)
+        assert isinstance(topo[0].op.scalar_op, scal.basic.Cast)
+
+    def test_upcast(self):
+        # Upcast followed by any other cast
+        x = T.fmatrix()
+        o = T.Elemwise(scal.Cast(scal.Scalar("complex128")))(x.astype("complex64"))
+        f = theano.function([x], o, mode=self.mode)
+        dx = numpy.random.rand(5, 4).astype("float32")
+        f(dx)
+        topo = f.maker.fgraph.toposort()
+        assert len(topo) == 1
+        assert isinstance(topo[0].op.scalar_op, scal.basic.Cast)
+
+        # Upcast followed by a downcast back to the base type
+        x = T.fmatrix()
+        o = T.Elemwise(scal.Cast(scal.Scalar("float32")))(x.astype("float64"))
+        f = theano.function([x], o, mode=self.mode)
+        dx = numpy.random.rand(5, 4).astype('float32')
+        f(dx)
+        topo = f.maker.fgraph.toposort()
+        assert len(topo) == 1
+        assert isinstance(topo[0].op, DeepCopyOp)
+
+        # Downcast followed by an upcast back to the base type
+        x = T.dmatrix()
+        o = T.Elemwise(scal.Cast(scal.Scalar("float64")))(x.astype("float32"))
+        f = theano.function([x], o, mode=self.mode)
+        dx = numpy.random.rand(5, 4)
+        f(dx)
+        topo = f.maker.fgraph.toposort()
+        assert len(topo) == 1
+        assert isinstance(topo[0].op.scalar_op, scal.basic.Composite)
 
 
 class T_func_inverse(unittest.TestCase):