Merge pull request #1429 from nouiz/err_msg_test_compute_value

theano/misc/check_blas.py

@@ -44,6 +44,10 @@ def execute(execute=True, verbose=True, M=2000, N=2000, K=2000,
         print '    compiledir=', theano.config.compiledir
         print '    floatX=', theano.config.floatX
         print '    device=', theano.config.device
+        print 'Some OS information:'
+        print '    sys.platform=', sys.platform
+        print '    sys.version=', sys.version
+        print '    sys.prefix=', sys.prefix
         print 'Some environment variables:'
         print '    MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
         print '    OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')

theano/misc/gnumpy_utils.py

@@ -106,8 +106,11 @@ try:
             strides = [1]
             for i in x.shape[::-1][:-1]:
                 strides.append(strides[-1]*i)
-            strides = tuple(strides[::-1])
-
+            strides = strides[::-1]
+            for i in range(len(strides)):
+                if x.shape[i] == 1:
+                    strides[i] = 0
+            strides = tuple(strides)
 
             import ctypes
             ptr_long = long(ctypes.cast(x._base.mat.data_device, ctypes.c_void_p).value)

theano/misc/tests/test_gnumpy_utils.py

@@ -63,3 +63,16 @@ def test2(shape=(3, 4, 5)):
     assert A_cnd.gpudata == B.gpudata
     v = numpy.asarray(B)
     assert (v == A).all()
+
+
+def test_broadcast_dims():
+    """
+    Test with some dimensions being 1.
+    CudaNdarray use 0 for strides for those dimensions.
+    """
+    test((1, 2, 3))
+    test((2, 1, 3))
+    test((2, 3, 1))
+    test2((1, 2, 3))
+    test2((2, 1, 3))
+    test2((2, 3, 1))

theano/tensor/basic.py

@@ -69,6 +69,13 @@ class ShapeError(Exception):
     pass
 
 
+class AsTensorError(TypeError):
+    """Raised when as_tensor_variable isn't able to create a
+    TensorVariable.
+    """
+    pass
+
+
 def check_equal_numpy(x, y):
     """
     Returns True iff x and y are equal (checks the dtype and
@@ -151,8 +158,8 @@ def as_tensor_variable(x, name=None, ndim=None):
        not possible.
 
     :Exceptions:
-     - `ValueError`: raised if an `Apply` with no default output is fetched
-     - `TypeError`: raised if `x` cannot be converted to a TensorType Variable
+     - `ValueError`: raised if an `Apply` with more then one output is fetched
+     - `AsTensorError`: raised if `x` cannot be converted to a TensorType Variable
 
     """
     if hasattr(x, '_as_TensorVariable'):
@@ -171,7 +178,7 @@ def as_tensor_variable(x, name=None, ndim=None):
             x = tensor_from_scalar(x)
 
         if not isinstance(x.type, TensorType):
-            raise TypeError(
+            raise AsTensorError(
                 "Variable type field must be a TensorType.", x, x.type)
 
         if ndim is None:
@@ -194,7 +201,7 @@ def as_tensor_variable(x, name=None, ndim=None):
             pass
 
     if isinstance(x, bool):
-        raise TypeError(
+        raise AsTensorError(
             "Cannot cast True or False as a tensor variable. Please use 1 or "
             "0. This error might be caused by using the == operator on "
             "Variables. v == w does not do what you think it does, "
@@ -207,7 +214,7 @@ def as_tensor_variable(x, name=None, ndim=None):
             str_x = str(x)
         except Exception:
             str_x = repr(x)
-        raise TypeError("Cannot convert %s to TensorType" % str_x, type(x))
+        raise AsTensorError("Cannot convert %s to TensorType" % str_x, type(x))
 
 # this has a different name, because _as_tensor_variable is the
 # function which ops use to upcast their arguments... this
@@ -1584,9 +1591,9 @@ class _tensor_py_operators:
         # Otherwise this will convert error when Theano flags
         # compute_test_value is used
         # Evidently, we need to catch NotImplementedError
-        # But we also need to catch TypeError
+        # TypeError from as_tensor_variable are caught in Elemwise.make_node
         # Oterwise TensorVariable * SparseVariable won't work!
-        except (NotImplementedError, TypeError):
+        except (NotImplementedError, AsTensorError):
             # We must return NotImplemented and not an
             # NotImplementedError or raise an NotImplementedError.
             # That way python will give a good error message like this
@@ -1599,7 +1606,7 @@ class _tensor_py_operators:
         # and the return value in that case
         try:
             return sub(self, other)
-        except (NotImplementedError, TypeError):
+        except (NotImplementedError, AsTensorError):
             return NotImplemented
 
     def __mul__(self, other):
@@ -1607,7 +1614,7 @@ class _tensor_py_operators:
         # and the return value in that case
         try:
             return mul(self, other)
-        except (NotImplementedError, TypeError):
+        except (NotImplementedError, AsTensorError):
             return NotImplemented
 
     def __div__(self, other):
@@ -1619,7 +1626,7 @@ class _tensor_py_operators:
             # This is to raise the exception that occurs when trying to divide
             # two integer arrays (currently forbidden).
             raise
-        except (NotImplementedError, TypeError):
+        except (NotImplementedError, AsTensorError):
             return NotImplemented
     if PY3:
         __truediv__ = __div__
@@ -1629,7 +1636,7 @@ class _tensor_py_operators:
         # adn the return value in that case
         try:
             return pow(self, other)
-        except (NotImplementedError, TypeError):
+        except (NotImplementedError, AsTensorError):
             return NotImplemented
 
     def __mod__(self, other):
@@ -1641,7 +1648,7 @@ class _tensor_py_operators:
             # This is to raise the exception that occurs when trying to compute
             # x % y with either x or y a complex number.
             raise
-        except (NotImplementedError, TypeError):
+        except (NotImplementedError, AsTensorError):
             return NotImplemented
 
     def __truediv__(self, other):

theano/tensor/elemwise.py

@@ -530,7 +530,6 @@ class Elemwise(Op):
         is left-completed to the greatest number of dimensions with 1s
         using DimShuffle.
         """
-
         inputs = map(as_tensor_variable, inputs)
         shadow = self.scalar_op.make_node(
                 *[Scalar(dtype=i.type.dtype)() for i in inputs])