It’s CSRNDArray.

KvStore doesn’t support it, but I have solved this problem.

The problem I am facing now is that creating a CSRNDArray is relatively slow.

```
def update(name, input, stored):
# the shape of stored is (num_device, keep_num*2)
# the shape of input is (1, keep_num*2+1)
# input[0] represent the device id
# input[1:keep_num+1] represent the value of sparse gradient
# input[keep_num+1:] represent the index of sparse gradient
stored[input[0]] = input[1:]
def _recover_grad_topk(pull_array, keep_num, array_size, recover_shape):
# the shape of pull_array is (num_device, keep_num*2)
# pull_array [:keep_num] represent the value of sparse gradient
# pull_array [keep_num:] represent the index of sparse gradient
grad_topk_sum = nd.sparse.zeros('csr', shape=(1, array_size), ctx=pull_array.context)
for grad_topk_array in pull_array:
grad_topk_saprse = nd.sparse.csr_matrix((grad_topk_array[:keep_num],
grad_topk_array[keep_num:].astype('int32'),
[0, keep_num]), ctx=grad_topk_array.context)
grad_topk_sum = grad_topk_sum+grad_topk_saprse
agg_grad_topk_array = nd.sparse.cast_storage(grad_topk_sum, stype='default').reshape(recover_shape)
return agg_grad_topk_array
...
kvstore.pull(name, pull_array_list, priority=-index)
for k, p in enumerate(zip(arg_list, pull_array_list)):
agg_grad_topk_array = _recover_grad_topk(pull_array_list, keep_num, arg_list.size, arg_list.shape)
```

But the recover process is time consuming