Extract reference for operator ADD_N to standalone header

Move the reference implementation to its own header so that micro
can use it without the unrelated depedencies of reference_ops.h.

PR step 2 for issue #46162

tensorflow/lite/kernels/internal/BUILD

@@ -449,6 +449,7 @@ cc_library(
     srcs = [],
     hdrs = [
         "reference/add.h",
+        "reference/add_n.h",
         "reference/arg_min_max.h",
         "reference/batch_matmul.h",
         "reference/binary_function.h",
@@ -558,6 +559,7 @@ cc_library(
     srcs = [],
     hdrs = [
         "reference/add.h",
+        "reference/add_n.h",
         "reference/arg_min_max.h",
         "reference/binary_function.h",
         "reference/ceil.h",

tensorflow/lite/kernels/internal/reference/add_n.h

@@ -0,0 +1,42 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_
+#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_
+
+#include "tensorflow/lite/kernels/internal/types.h"
+
+namespace tflite {
+namespace reference_ops {
+
+// T is expected to be either float or int.
+template <typename T>
+inline void AddN(const RuntimeShape& input_shape, const size_t num_inputs,
+                 T* const* input_data, T* output_data) {
+  // All inputs and output should have the same shape, this is checked during
+  // Prepare stage.
+  const size_t size = input_shape.FlatSize();
+  for (int i = 0; i < size; ++i) {
+    T x = 0;
+    for (int j = 0; j < num_inputs; ++j) {
+      x += input_data[j][i];
+    }
+    output_data[i] = x;
+  }
+}
+
+}  // namespace reference_ops
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_

tensorflow/lite/kernels/internal/reference/reference_ops.h

@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/quantization_util.h"
 #include "tensorflow/lite/kernels/internal/reference/add.h"
+#include "tensorflow/lite/kernels/internal/reference/add_n.h"
 #include "tensorflow/lite/kernels/internal/reference/arg_min_max.h"
 #include "tensorflow/lite/kernels/internal/reference/binary_function.h"
 #include "tensorflow/lite/kernels/internal/reference/ceil.h"
@@ -253,22 +254,6 @@ inline void QuantizeLeakyRelu(const LeakyReluParams& params,
   }
 }
 
-// T is expected to be either float or int.
-template <typename T>
-inline void AddN(const RuntimeShape& input_shape, const size_t num_inputs,
-                 T* const* input_data, T* output_data) {
-  // All inputs and output should have the same shape, this is checked during
-  // Prepare stage.
-  const size_t size = input_shape.FlatSize();
-  for (int i = 0; i < size; ++i) {
-    T x = 0;
-    for (int j = 0; j < num_inputs; ++j) {
-      x += input_data[j][i];
-    }
-    output_data[i] = x;
-  }
-}
-
 // TODO(jiawen): We can implement BroadcastMul on buffers of arbitrary
 // dimensionality if the runtime code does a single loop over one dimension
 // that handles broadcasting as the base case. The code generator would then