Move theano.sparse_grad to theano.sparse.sparse_grad

doc/library/index.txt

@@ -59,5 +59,3 @@ There are also some top-level imports that you might find more convenient:
     Works like :func:`tensor.dot` for both sparse and dense matrix products
 
 .. autofunction:: theano.clone_replace
-
-.. autofunction:: theano.sparse_grad

doc/library/sparse/index.txt

@@ -297,4 +297,4 @@ List of Implemented Operations
 .. automodule:: theano.sparse.basic
     :members:
 
-.. autofunction:: tests.sparse.test_basic.sparse_random_inputs
+.. autofunction:: theano.sparse.sparse_grad

tests/sparse/test_basic.py

@@ -656,7 +656,7 @@ class TestConstructSparseFromList:
         # USER INTERFACE
         m = matrix()
         v = ivector()
-        sub = theano.sparse_grad(m[v])
+        sub = theano.sparse.sparse_grad(m[v])
         g = theano.grad(sub.sum(), m)
         assert isinstance(g.owner.op, ConstructSparseFromList)
 
@@ -675,7 +675,7 @@ class TestConstructSparseFromList:
         shared_v = theano.shared(valv)
 
         def fn(m):
-            return theano.sparse_grad(m[shared_v])
+            return theano.sparse.sparse_grad(m[shared_v])
 
         verify_grad_sparse(fn, [valm])
 
@@ -691,7 +691,7 @@ class TestConstructSparseFromList:
 
             # Test that we raise an error, as we can't create a sparse
             # grad from tensors that don't have 2 dimensions.
-            sub = theano.sparse_grad(sub)
+            sub = theano.sparse.sparse_grad(sub)
             with pytest.raises(TypeError):
                 theano.grad(sub.sum(), t)
 

theano/__init__.py

@@ -129,23 +129,6 @@ def get_scalar_constant_value(v):
     return tensor.get_scalar_constant_value(v)
 
 
-def sparse_grad(var):
-    """This function return a new variable whose gradient will be
-    stored in a sparse format instead of dense.
-
-    Currently only variable created by AdvancedSubtensor1 is supported.
-    i.e. a_tensor_var[an_int_vector].
-
-    .. versionadded:: 0.6rc4
-    """
-    from theano.tensor.subtensor import AdvancedSubtensor1
-
-    assert isinstance(var.owner.op, AdvancedSubtensor1)
-
-    ret = var.owner.op.__class__(sparse_grad=True)(*var.owner.inputs)
-    return ret
-
-
 import theano.tensor.random.var
 from theano.graph.basic import clone_replace
 from theano.scan import checkpoints

theano/sparse/__init__.py

@@ -9,10 +9,26 @@ except ImportError:
     enable_sparse = False
     warn("SciPy can't be imported.  Sparse matrix support is disabled.")
 
-from theano.sparse.type import *
+from theano.sparse.type import SparseType, _is_sparse
 
 
 if enable_sparse:
     from theano.sparse import opt, sharedvar
     from theano.sparse.basic import *
     from theano.sparse.sharedvar import sparse_constructor as shared
+
+    def sparse_grad(var):
+        """This function return a new variable whose gradient will be
+        stored in a sparse format instead of dense.
+
+        Currently only variable created by AdvancedSubtensor1 is supported.
+        i.e. a_tensor_var[an_int_vector].
+
+        .. versionadded:: 0.6rc4
+        """
+        from theano.tensor.subtensor import AdvancedSubtensor1
+
+        assert isinstance(var.owner.op, AdvancedSubtensor1)
+
+        ret = var.owner.op.__class__(sparse_grad=True)(*var.owner.inputs)
+        return ret

theano/tensor/type.py

@@ -71,7 +71,7 @@ class TensorType(CType):
             warnings.warn(
                 "You use an old interface to"
                 " AdvancedSubtensor1 sparse_grad. Now use"
-                " theano.sparse_grad(a_tensor[an_int_vector]).",
+                " theano.sparse.sparse_grad(a_tensor[an_int_vector]).",
                 category=DeprecationWarning,
             )