[XLA] Clean up clang-tidy warnings in reference_util

By marking std::function parameters as const& where applicable; this also makes it more consistent with the other utils that use std::function parameters.

PiperOrigin-RevId: 162365501

2 files changed, 9 insertions, 9 deletions

diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index 138e360c29..1495790870 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -578,7 +578,7 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated(
 /* static */ std::unique_ptr<std::vector<float>>
 ReferenceUtil::ReduceToColArray2D(
     const Array2D<float>& matrix, float init,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
   auto result = MakeUnique<std::vector<float>>();
@@ -595,7 +595,7 @@ ReferenceUtil::ReduceToColArray2D(
 /* static */ std::unique_ptr<std::vector<float>>
 ReferenceUtil::ReduceToRowArray2D(
     const Array2D<float>& matrix, float init,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   int64 rows = matrix.height();
   int64 cols = matrix.width();
   auto result = MakeUnique<std::vector<float>>();
@@ -612,7 +612,7 @@ ReferenceUtil::ReduceToRowArray2D(
 /*static*/ std::vector<float> ReferenceUtil::Reduce4DTo1D(
     const Array4D<float>& array, float init,
     tensorflow::gtl::ArraySlice<int64> dims,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   std::vector<float> result;
   CHECK_EQ(dims.size(), 3);
   const std::set<int64> dim_set(dims.begin(), dims.end());
@@ -682,7 +682,7 @@ ReferenceUtil::ReduceToRowArray2D(
 /* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::Reduce3DTo2D(
     const Array3D<float>& array, float init,
     tensorflow::gtl::ArraySlice<int64> dims,
-    std::function<float(float, float)> reduce_function) {
+    const std::function<float(float, float)>& reduce_function) {
   CHECK_EQ(dims.size(), 1);
   int64 rows = dims[0] == 0 ? array.n2() : array.n1();
   int64 cols = dims[0] == 2 ? array.n2() : array.n3();
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index 41ef1f96ab..5b2ddf4d7f 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -88,21 +88,21 @@ class ReferenceUtil {
   // to apply for each reduction step.
   static std::unique_ptr<std::vector<float>> ReduceToColArray2D(
       const Array2D<float>& matrix, float init,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Returns the result of reducing a matrix to a row vector. init is the
   // initial value for the reduce operation, and reduce_function is the function
   // to apply for each reduction step.
   static std::unique_ptr<std::vector<float>> ReduceToRowArray2D(
       const Array2D<float>& matrix, float init,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Performs a R2=>R1 reduction by reducing away the dimension specified in
   // 'dimension_to_reduce'.
   template <typename T>
   static std::vector<T> ReduceR2ToR1(const Array2D<T>& input,
                                      int dimension_to_reduce, T init,
-                                     std::function<T(T, T)> freduce) {
+                                     const std::function<T(T, T)>& freduce) {
     std::vector<T> result(dimension_to_reduce == 0 ? input.n2() : input.n1(),
                           init);
     for (int i0 = 0; i0 < input.n1(); ++i0) {
@@ -119,7 +119,7 @@ class ReferenceUtil {
   static std::vector<float> Reduce4DTo1D(
       const Array4D<float>& array, float init,
       tensorflow::gtl::ArraySlice<int64> dims,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Broadcast 1D dimension to 4D, from the dimension `broadcast_from_dim`.
   static std::unique_ptr<Array4D<float>> Broadcast1DTo4D(
@@ -131,7 +131,7 @@ class ReferenceUtil {
   static std::unique_ptr<Array2D<float>> Reduce3DTo2D(
       const Array3D<float>& array, float init,
       tensorflow::gtl::ArraySlice<int64> dims,
-      std::function<float(float, float)> reduce_function);
+      const std::function<float(float, float)>& reduce_function);
 
   // Applies map_function to each element in the input (2D array) and returns
   // the result.