haojin2 commented on a change in pull request #10208: [MXNET-117] Sparse 
operator broadcast_mul/div(csr, dense) = csr
URL: https://github.com/apache/incubator-mxnet/pull/10208#discussion_r179242273
 
 

 ##########
 File path: python/mxnet/ndarray/sparse.py
 ##########
 @@ -1159,6 +1186,322 @@ def _ndarray_cls(handle, writable=True, 
stype=_STORAGE_TYPE_UNDEFINED):
 _set_ndarray_class(_ndarray_cls)
 
 
+def add(lhs, rhs):
+    """Returns element-wise sum of the input arrays with broadcasting.
+
+    Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
+    ``mx.nd.broadcast_plus(lhs, rhs)`` when shapes of lhs and rhs do not
+    match. If lhs.shape == rhs.shape, this is equivalent to
+    ``mx.nd.elemwise_add(lhs, rhs)``
+
+    .. note::
+
+        If the corresponding dimensions of two arrays have the same size or 
one of them has size 1,
+        then the arrays are broadcastable to a common shape.abs
+
+    Parameters
+    ----------
+    lhs : scalar or array
+        First array to be added.
+    rhs : scalar or array
+         Second array to be added.
+        If ``lhs.shape != rhs.shape``, they must be
+        broadcastable to a common shape.
+
+    Returns
+    -------
+    NDArray
+        The element-wise sum of the input arrays.
+
+    Examples
+    --------
+    >>> x = mx.nd.ones((2,3)).tostype('csr')
+    >>> y = mx.nd.arange(2).reshape((2,1))
+    >>> z = mx.nd.arange(2).reshape((1,2))
+    >>> x.asnumpy()
+    array([[ 1.,  1.,  1.],
+           [ 1.,  1.,  1.]], dtype=float32)
+    >>> y.asnumpy()
+    array([[ 0.],
+           [ 1.]], dtype=float32)
+    >>> z.asnumpy()
+    array([[ 0.,  1.]], dtype=float32)
+    >>> (x+2).asnumpy()
+    array([[ 3.,  3.,  3.],
+           [ 3.,  3.,  3.]], dtype=float32)
+    >>> (x+y).asnumpy()
+    array([[ 1.,  1.,  1.],
+           [ 2.,  2.,  2.]], dtype=float32)
+    >>> mx.nd.add(x,y).asnumpy()
+    array([[ 1.,  1.,  1.],
+           [ 2.,  2.,  2.]], dtype=float32)
+    >>> (z + y).asnumpy()
+    array([[ 0.,  1.],
+           [ 1.,  2.]], dtype=float32)
+    """
+    # pylint: disable= no-member, protected-access
+    if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == 
rhs.shape:
+        return _ufunc_helper(
+            lhs,
+            rhs,
+            op.elemwise_add,
+            operator.add,
+            _internal._plus_scalar,
+            None)
+
+    return _ufunc_helper(
+        lhs,
+        rhs,
+        op.broadcast_add,
+        operator.add,
+        _internal._plus_scalar,
+        None)
+    # pylint: enable= no-member, protected-access
+
+
+def subtract(lhs, rhs):
+    """Returns element-wise difference of the input arrays with broadcasting.
+
+    Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
+    ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not
+    match. If lhs.shape == rhs.shape, this is equivalent to
+    ``mx.nd.elemwise_sub(lhs, rhs)``
+
+    .. note::
+
+        If the corresponding dimensions of two arrays have the same size or 
one of them has size 1,
+        then the arrays are broadcastable to a common shape.
+
+    Parameters
+    ----------
+    lhs : scalar or array
+        First array to be subtracted.
+    rhs : scalar or array
+         Second array to be subtracted.
+        If ``lhs.shape != rhs.shape``, they must be
+        broadcastable to a common shape.__spec__
+
+    Returns
+    -------
+    NDArray
+        The element-wise difference of the input arrays.
+
+    Examples
+    --------
+    >>> x = mx.nd.ones((2,3)).tostype('csr')
+    >>> y = mx.nd.arange(2).reshape((2,1))
 
 Review comment:
   This is multiply, subtract has been fixed above

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