Add helper functions for specifying the "expected" values

theano/tensor/tests/test_basic.py

@@ -189,6 +189,50 @@ def safe_make_node(op, *inputs):
         return node.owner
 
 
+def upcast_float16_ufunc(fn):
+    """Decorator that enforces computation is not done in float16 by NumPy.
+
+    Some ufuncs in NumPy will compute float values on int8 and uint8
+    in half-precision (float16), which is not enough, and not compatible
+    with the C code.
+
+    :param fn: numpy ufunc
+    :returns: function similar to fn.__call__, computing the same
+        value with a minimum floating-point precision of float32
+    """
+    def ret(*args, **kwargs):
+        out_dtype = numpy.find_common_type(
+            [a.dtype for a in args], [numpy.float16])
+        if out_dtype == 'float16':
+            # Force everything to float32
+            sig = 'f' * fn.nin + '->' + 'f' * fn.nout
+            kwargs.update(sig=sig)
+        return fn(*args, **kwargs)
+
+    return ret
+
+
+def upcast_int8_nfunc(fn):
+    """Decorator that upcasts input of dtype int8 to float32.
+
+    This is so that floating-point computation is not carried using
+    half-precision (float16), as some NumPy functions do.
+
+    :param fn: function computing a floating-point value from inputs
+    :returns: function similar to fn, but upcasting its uint8 and int8
+        inputs before carrying out the computation.
+    """
+    def ret(*args, **kwargs):
+        args = list(args)
+        for i, a in enumerate(args):
+            if getattr(a, 'dtype', None) in ('int8', 'uint8'):
+                args[i] = a.astype('float32')
+
+        return fn(*args, **kwargs)
+
+    return ret
+
+
 def makeTester(name, op, expected, checks=None, good=None, bad_build=None,
                bad_runtime=None, grad=None, mode=None, grad_rtol=None,
                eps=1e-10, skip=False, test_memmap=True, check_name=True):
@@ -321,7 +365,8 @@ def makeTester(name, op, expected, checks=None, good=None, bad_build=None,
                     expecteds = self.expected(*inputs)
                     eps = 1e-10
 
-                if any([i.dtype == 'float32' for i in inputs]):
+                if any([i.dtype in ('float32', 'int8', 'uint8')
+                        for i in inputs]):
                     eps = 1e-6
                 eps = numpy.max([eps, _eps])