change indptr to _indptr temporarily. add const ref to fname

python/mxnet/sparse_ndarray.py

@@ -288,7 +288,7 @@ def _aux_type(self, i):
         return _DTYPE_MX_TO_NP[aux_type.value]
 
     @property
-    def values(self):
+    def _values(self):
         """The values array of the SparseNDArray. This is a read-only view of the values array.
         They reveal internal implementation details and should be used with care.
 
@@ -300,7 +300,7 @@ def values(self):
         return self._data()
 
     @property
-    def indices(self):
+    def _indices(self):
         """The indices array of the SparseNDArray. This is a read-only view of the indices array.
         They reveal internal implementation details and should be used with care.
 
@@ -317,7 +317,7 @@ def indices(self):
         raise Exception("unknown storage type " + stype)
 
     @property
-    def indptr(self):
+    def _indptr(self):
         """The indptr array of the SparseNDArray with `csr` storage type.
         This is a read-only view of the indptr array.
         They reveal internal implementation details and should be used with care.
@@ -602,6 +602,8 @@ def zeros(storage_type, shape, ctx=None, dtype=None, aux_types=None):
 
 def _ndarray_cls(handle):
     stype = _storage_type(handle)
+    # TODO(haibin) in the long run, we want to have CSRNDArray and RowSparseNDArray which
+    # inherit from SparseNDArray
     return NDArray(handle) if stype == 'default_storage' else SparseNDArray(handle)
 
 _init_ndarray_module(_ndarray_cls, "mxnet")

src/operator/operator_common.h

@@ -325,7 +325,7 @@ void FCompExFallback(const nnvm::NodeAttrs& attrs,
                      const std::vector<OpReqType>& req,
                      const std::vector<NDArray>& outputs,
                      FCompute fcompute,
-                     const std::string fname) {
+                     const std::string& fname) {
   std::vector<TBlob> in_blobs, out_blobs;
   std::vector<NDArray> tmps;
   common::GetInputBlobs<xpu>(inputs, &in_blobs, &tmps, ctx, true);

tests/python/unittest/test_sparse_ndarray.py

@@ -89,9 +89,9 @@ def check_sparse_nd_prop_rsp():
     shape = rand_shape_2d()
     nd, (v, idx) = rand_sparse_ndarray(shape, storage_type)
     assert(nd._num_aux == 1)
-    assert(nd.indices.dtype == np.int32)
+    assert(nd._indices.dtype == np.int32)
     assert(nd.storage_type == 'row_sparse')
-    assert_almost_equal(nd.indices.asnumpy(), idx)
+    assert_almost_equal(nd._indices.asnumpy(), idx)
 
 def test_sparse_nd_basic():
     def check_rsp_creation(values, indices, shape):
@@ -101,13 +101,13 @@ def check_rsp_creation(values, indices, shape):
         dns[3] = mx.nd.array(values[1])
         assert_almost_equal(rsp.asnumpy(), dns.asnumpy())
         indices = mx.nd.array(indices).asnumpy()
-        assert_almost_equal(rsp.indices.asnumpy(), indices)
+        assert_almost_equal(rsp._indices.asnumpy(), indices)
 
     def check_csr_creation(shape):
         csr, (indptr, indices, values) = rand_sparse_ndarray(shape, 'csr')
-        assert_almost_equal(csr.indptr.asnumpy(), indptr)
-        assert_almost_equal(csr.indices.asnumpy(), indices)
-        assert_almost_equal(csr.values.asnumpy(), values)
+        assert_almost_equal(csr._indptr.asnumpy(), indptr)
+        assert_almost_equal(csr._indices.asnumpy(), indices)
+        assert_almost_equal(csr._values.asnumpy(), values)
 
     shape = (4,2)
     values = np.random.rand(2,2)
@@ -147,8 +147,6 @@ def check_sparse_nd_csr_slice(shape):
         A2 = A.asnumpy()
         start = rnd.randint(0, shape[0] - 1)
         end = rnd.randint(start + 1, shape[0])
-        values = A[start:end].values
-        indptr = A[start:end].indptr
         assert same(A[start:end].asnumpy(), A2[start:end])
 
     shape = (rnd.randint(2, 10), rnd.randint(1, 10))