1 files changed, 93 insertions, 0 deletions
diff --git a/runtimes/libs/cker/include/cker/operation/Concatenation.h b/runtimes/libs/cker/include/cker/operation/Concatenation.h
new file mode 100644
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--- /dev/null
+++ b/runtimes/libs/cker/include/cker/operation/Concatenation.h
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2017 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 __NNFW_CKER_CONCATENATION_H__
+#define __NNFW_CKER_CONCATENATION_H__
+
+#include <cstdint>
+
+#include "cker/Shape.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+struct ConcatenationParams
+{
+  int8_t axis;
+  const int32_t *input_zeropoint;
+  const float *input_scale;
+  uint16_t inputs_count;
+  int32_t output_zeropoint;
+  float output_scale;
+};
+
+template <typename Scalar>
+inline void Concatenation(const ConcatenationParams &params, const Shape *const *input_shapes,
+                          const Scalar *const *input_data, const Shape &output_shape,
+                          Scalar *output_data)
+{
+  int axis = params.axis;
+  int inputs_count = params.inputs_count;
+  const int concat_dimensions = output_shape.DimensionsCount();
+  assert(axis < concat_dimensions);
+
+  int64_t concat_size = 0;
+  for (int i = 0; i < inputs_count; i++)
+  {
+    assert(input_shapes[i]->DimensionsCount() == concat_dimensions);
+    for (int j = 0; j < concat_dimensions; j++)
+    {
+      if (j != axis)
+      {
+        auto dim_checked = MatchingDim(*input_shapes[i], j, output_shape, j);
+        UNUSED_RELEASE(dim_checked);
+      }
+    }
+    concat_size += input_shapes[i]->Dims(axis);
+  }
+  assert(concat_size == output_shape.Dims(axis));
+  int64_t outer_size = 1;
+  for (int i = 0; i < axis; ++i)
+  {
+    outer_size *= output_shape.Dims(i);
+  }
+  // For all input arrays,
+  // FlatSize() = outer_size * Dims(axis) * base_inner_size;
+  int64_t base_inner_size = 1;
+  for (int i = axis + 1; i < concat_dimensions; ++i)
+  {
+    base_inner_size *= output_shape.Dims(i);
+  }
+
+  Scalar *output_ptr = output_data;
+  for (int k = 0; k < outer_size; k++)
+  {
+    for (int i = 0; i < inputs_count; ++i)
+    {
+      const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size;
+      memcpy(output_ptr, input_data[i] + k * copy_size, copy_size * sizeof(Scalar));
+      output_ptr += copy_size;
+    }
+  }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_CONCATENATION_H__