Back to initial contiguousity-testing and make it more precise.

theano/tensor/blas.py

@@ -2149,8 +2149,14 @@ class BatchedDot(Op):
         fail = sub["fail"]
 
         # generate contiguity condition
-        def contiguous(var):
-            return "(PyArray_IS_C_CONTIGUOUS(%s) || PyArray_IS_F_CONTIGUOUS(%s))" % (var, var)
+        def contiguous(var, ndim):
+            strides = "PyArray_STRIDES(%s)" % var
+            return " && ".join([
+                " && ".join("{strides}[{i}] > 0 && {strides}[{i}] % type_size == 0"
+                            .format(strides=strides, i=i) for i in range(ndim)),
+                "(%s)" % (" || ".join("{strides}[{i}] == type_size"
+                                     .format(strides=strides, i=i) for i in range(1, ndim)) or '1'),
+            ])
 
         x_ndim, y_ndim, z_ndim = node.inputs[0].ndim, node.inputs[1].ndim, node.outputs[0].ndim
 
@@ -2165,7 +2171,7 @@ class BatchedDot(Op):
         z_shape_correct = " && ".join("PyArray_DIMS(%s)[%i] == %s"
                                       % (_z, i, dim) for i, dim in enumerate(z_dims))
         z_shape = ", ".join(z_dims)
-        z_contiguous = contiguous(_z)
+        z_contiguous = contiguous(_z, z_ndim)
         allocate = """
             if (NULL == %(_z)s || !(%(z_shape_correct)s)  || !(%(z_contiguous)s))
             {
@@ -2183,8 +2189,8 @@ class BatchedDot(Op):
 
         # code to reallocate inputs contiguously if necessary
         contiguate = []
-        for var in (_x, _y):
-            _contiguous = contiguous(var)
+        for var, ndim in [(_x, x_ndim), (_y, y_ndim)]:
+            _contiguous = contiguous(var, ndim)
             contiguate.append("""
                 if (!(%(_contiguous)s)) {
                     PyArrayObject * _copy = (PyArrayObject *) PyArray_Copy(%(var)s);

theano/tensor/tests/test_basic.py

@@ -2783,16 +2783,22 @@ def test_batched_dot_not_contiguous():
     Z = batched_dot(X, W)
     f = function([X, W], Z)
 
+    w = np_genarray(30, 10, 5)
     reversed_x_container = np_genarray(20, 40, 30)
     x_container = reversed_x_container.T
-    x = x_container[::1, ::2, ::2]
-    assert x.shape == (30, 20, 10)
-    assert x.strides[0] == numpy.dtype(floatX).itemsize
-    assert not (x.flags['C_CONTIGUOUS'] or x.flags['F_CONTIGUOUS'])
-    w = np_genarray(30, 10, 5)
-    result = f(x, w)
-    ref_result = numpy.asarray(list(numpy.dot(u, v) for u, v in zip(x, w)))
-    utt.assert_allclose(ref_result, result)
+
+    def check_first_dim(inverted):
+        direction = -1 if inverted else 1
+        x = x_container[::direction, ::2, ::2]
+        assert x.shape == (30, 20, 10)
+        assert x.strides[0] == direction * numpy.dtype(floatX).itemsize
+        assert not (x.flags['C_CONTIGUOUS'] or x.flags['F_CONTIGUOUS'])
+        result = f(x, w)
+        ref_result = numpy.asarray(list(numpy.dot(u, v) for u, v in zip(x, w)))
+        utt.assert_allclose(ref_result, result)
+
+    for inverted in (0, 1):
+        yield (check_first_dim, inverted)
 
 
 def test_batched_tensordot():