1 files changed, 69 insertions, 0 deletions
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 9722f0ee9a..6aca2c3b01 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -710,6 +710,75 @@ a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
 b: `ndims`-D Tensor.  With shape `a_shape`.
 )doc");
 
+REGISTER_OP("SparseReduceMax")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output: T")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+instead of a sparse one.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+output: `R-K`-D.  The reduced Tensor.
+)doc");
+
+REGISTER_OP("SparseReduceMaxSparse")
+    .Input("input_indices: int64")
+    .Input("input_values: T")
+    .Input("input_shape: int64")
+    .Input("reduction_axes: int32")
+    .Attr("keep_dims: bool = False")
+    .Output("output_indices: int64")
+    .Output("output_values: T")
+    .Output("output_shape: int64")
+    .Attr("T: realnumbertype")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Doc(R"doc(
+Computes the max of elements across dimensions of a SparseTensor.
+
+This Op takes a SparseTensor and is the sparse counterpart to
+`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
+SparseTensor.
+
+Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+with length 1.
+
+If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+with a single element is returned.  Additionally, the axes can be negative,
+which are interpreted according to the indexing rules in Python.
+
+input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
+  SparseTensor, possibly not in canonical ordering.
+input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
+input_shape: 1-D.  Shape of the input SparseTensor.
+reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
+keep_dims: If true, retain reduced dimensions with length 1.
+)doc");
+
 REGISTER_OP("SparseReduceSum")
     .Input("input_indices: int64")
     .Input("input_values: T")