mxnet.kvstore.KVStore.row_sparse_pull¶

KVStore.row_sparse_pull(key, out=None, priority=0, row_ids=None)[source]¶

Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull is invoked just once and the result is broadcast to all the rest of outputs.

row_sparse_pull is executed asynchronously after all previous pull/row_sparse_pull calls and the last push call for the same input key(s) are finished.

The returned values are guaranteed to be the latest values in the store.

Parameters

key (str, int, or sequence of str or int) – Keys.
out (RowSparseNDArray or list of RowSparseNDArray or list of list of RowSparseNDArray) – Values corresponding to the keys. The stype is expected to be row_sparse
priority (int, optional) – The priority of the pull operation. Higher priority pull operations are likely to be executed before other pull actions.
row_ids (NDArray or list of NDArray) – The row_ids for which to pull for each value. Each row_id is an 1-D NDArray whose values don’t have to be unique nor sorted.

Examples

>>> shape = (3, 3)
>>> kv.init('3', mx.nd.ones(shape).tostype('row_sparse'))
>>> a = mx.nd.sparse.zeros('row_sparse', shape)
>>> row_ids = mx.nd.array([0, 2], dtype='int64')
>>> kv.row_sparse_pull('3', out=a, row_ids=row_ids)
>>> print a.asnumpy()
[[ 1.  1.  1.]
[ 0.  0.  0.]
[ 1.  1.  1.]]
>>> duplicate_row_ids = mx.nd.array([2, 2], dtype='int64')
>>> kv.row_sparse_pull('3', out=a, row_ids=duplicate_row_ids)
>>> print a.asnumpy()
[[ 0.  0.  0.]
[ 0.  0.  0.]
[ 1.  1.  1.]]
>>> unsorted_row_ids = mx.nd.array([1, 0], dtype='int64')
>>> kv.row_sparse_pull('3', out=a, row_ids=unsorted_row_ids)
>>> print a.asnumpy()
[[ 1.  1.  1.]
[ 1.  1.  1.]
[ 0.  0.  0.]]