[onert-micro] Add Concatenation kernel

This pr adds first Concatenation kernel with common PALUtils.

ONE-DCO-1.0-Signed-off-by: Artem Balyshev <[email protected]>

onert-micro/onert-micro/include/pal/common/PALConcatenation.h

@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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 ONERT_MICRO_EXECUTE_PAL_CONCATENATION_H
+#define ONERT_MICRO_EXECUTE_PAL_CONCATENATION_H
+
+#include "PALUtils.h"
+
+namespace onert_micro
+{
+namespace execute
+{
+namespace pal
+{
+template <typename Scalar>
+OMStatus Concatenation(core::ConcatenationParams &params, std::vector<uint32_t> &input_shapes,
+                       std::vector<const Scalar *> &input_data,
+                       const core::OMRuntimeShape &output_shape, Scalar *output_data)
+{
+  int axis = params.axis;
+  int inputs_count = params.num_inputs;
+  const int concat_dimensions = output_shape.dimensionsCount();
+
+  int64_t concat_size = 0;
+  for (int i = 0; i < inputs_count; i++)
+  {
+    concat_size += input_shapes[i];
+  }
+  int64_t outer_size = 1;
+  for (int i = 0; i < axis; ++i)
+  {
+    outer_size *= output_shape.dims(i);
+  }
+  // For all input arrays,
+  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] * base_inner_size;
+      const Scalar *input_ptr = input_data[i] + k * copy_size;
+      memcpy(output_ptr, input_ptr, copy_size * sizeof(Scalar));
+      output_ptr += copy_size;
+    }
+  }
+
+  return Ok;
+}
+
+} // namespace pal
+} // namespace execute
+} // namespace onert_micro
+
+#endif // ONERT_MICRO_EXECUTE_PAL_CONCATENATION_H

onert-micro/onert-micro/include/pal/common/PALUtils.h

@@ -0,0 +1,219 @@
+/*
+ * Copyright (c) 2024 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 ONERT_MICRO_EXECUTE_PAL_UTILS_H
+#define ONERT_MICRO_EXECUTE_PAL_UTILS_H
+
+#include <cassert>
+
+namespace onert_micro
+{
+namespace execute
+{
+namespace pal
+{
+
+// Table of sigmoid(i/24) at 0.16 format - 256 elements.
+// We use combined sigmoid and tanh look-up table, since
+// tanh(x) = 2*sigmoid(2*x) -1.
+// Both functions are symmetric, so the LUT table is only needed
+// for the absolute value of the input.
+static const uint16_t sigmoid_table_uint16[256] = {
+  32768, 33451, 34133, 34813, 35493, 36169, 36843, 37513, 38180, 38841, 39498, 40149, 40794, 41432,
+  42064, 42688, 43304, 43912, 44511, 45102, 45683, 46255, 46817, 47369, 47911, 48443, 48964, 49475,
+  49975, 50464, 50942, 51409, 51865, 52311, 52745, 53169, 53581, 53983, 54374, 54755, 55125, 55485,
+  55834, 56174, 56503, 56823, 57133, 57433, 57724, 58007, 58280, 58544, 58800, 59048, 59288, 59519,
+  59743, 59959, 60168, 60370, 60565, 60753, 60935, 61110, 61279, 61441, 61599, 61750, 61896, 62036,
+  62172, 62302, 62428, 62549, 62666, 62778, 62886, 62990, 63090, 63186, 63279, 63368, 63454, 63536,
+  63615, 63691, 63765, 63835, 63903, 63968, 64030, 64090, 64148, 64204, 64257, 64308, 64357, 64405,
+  64450, 64494, 64536, 64576, 64614, 64652, 64687, 64721, 64754, 64786, 64816, 64845, 64873, 64900,
+  64926, 64950, 64974, 64997, 65019, 65039, 65060, 65079, 65097, 65115, 65132, 65149, 65164, 65179,
+  65194, 65208, 65221, 65234, 65246, 65258, 65269, 65280, 65291, 65301, 65310, 65319, 65328, 65337,
+  65345, 65352, 65360, 65367, 65374, 65381, 65387, 65393, 65399, 65404, 65410, 65415, 65420, 65425,
+  65429, 65433, 65438, 65442, 65445, 65449, 65453, 65456, 65459, 65462, 65465, 65468, 65471, 65474,
+  65476, 65479, 65481, 65483, 65485, 65488, 65489, 65491, 65493, 65495, 65497, 65498, 65500, 65501,
+  65503, 65504, 65505, 65507, 65508, 65509, 65510, 65511, 65512, 65513, 65514, 65515, 65516, 65517,
+  65517, 65518, 65519, 65520, 65520, 65521, 65522, 65522, 65523, 65523, 65524, 65524, 65525, 65525,
+  65526, 65526, 65526, 65527, 65527, 65528, 65528, 65528, 65529, 65529, 65529, 65529, 65530, 65530,
+  65530, 65530, 65531, 65531, 65531, 65531, 65531, 65532, 65532, 65532, 65532, 65532, 65532, 65533,
+  65533, 65533, 65533, 65533, 65533, 65533, 65533, 65534, 65534, 65534, 65534, 65534, 65534, 65534,
+  65534, 65534, 65534, 65535};
+
+inline std::int32_t saturatingRoundingDoublingHighMul(std::int32_t a, std::int32_t b)
+{
+  bool overflow = a == b && a == std::numeric_limits<std::int32_t>::min();
+  std::int64_t a_64(a);
+  std::int64_t b_64(b);
+  std::int64_t ab_64 = a_64 * b_64;
+  std::int32_t nudge = ab_64 >= 0 ? (1 << 30) : (1 - (1 << 30));
+  std::int32_t ab_x2_high32 = static_cast<std::int32_t>((ab_64 + nudge) / (1ll << 31));
+  return overflow ? std::numeric_limits<std::int32_t>::max() : ab_x2_high32;
+}
+
+// Correctly-rounded-to-nearest division by a power-of-two.
+// Also known as a rounding arithmetic right shift.
+inline int32_t roundingDivideByPOT(int32_t x, int32_t exponent)
+{
+  assert(exponent >= 0);
+  assert(exponent <= 31);
+  const int32_t mask = int32_t((1ll << exponent) - 1);
+  const int32_t zero = int32_t(0);
+  const int32_t one = int32_t(1);
+  const int32_t remainder = x & mask;
+  const int32_t threshold = (mask >> 1) + ((x < zero ? one : zero) & one);
+  return (x >> exponent) + ((remainder > threshold ? one : zero) & one);
+}
+
+inline int32_t multiplyByQuantizedMultiplier(int32_t x, int32_t quantized_multiplier, int shift)
+{
+  int left_shift = shift > 0 ? shift : 0;
+  int right_shift = shift > 0 ? 0 : -shift;
+  return roundingDivideByPOT(
+    saturatingRoundingDoublingHighMul(x * (1 << left_shift), quantized_multiplier), right_shift);
+}
+
+inline int32_t multiplyByQuantizedMultiplierSmallerThanOneExp(int32_t x,
+                                                              int32_t quantized_multiplier,
+                                                              int left_shift)
+{
+  return roundingDivideByPOT(saturatingRoundingDoublingHighMul(x, quantized_multiplier),
+                             -left_shift);
+}
+
+template <typename P> inline void getActivationParams(const P &params, int32_t *min, int32_t *max)
+{
+  *min = params.int32_activation_min;
+  *max = params.int32_activation_max;
+}
+
+template <typename P> inline void getActivationParams(const P &params, float *min, float *max)
+{
+  *min = params.float_activation_min;
+  *max = params.float_activation_max;
+}
+
+template <typename P> inline void getActivationParams(const P &params, int64_t *min, int64_t *max)
+{
+  *min = params.int64_activation_min;
+  *max = params.int64_activation_max;
+}
+
+// Gets offset of index if reducing on axis. When reducing, the flattened offset
+// will not change, if the input index changes on the given axis. For example,
+// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0,
+// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened
+// offset.
+inline size_t reducedOutputOffset(const int32_t num_dims, const int32_t *dims, const int32_t *index,
+                                  const int32_t num_axis, const int32_t *axis)
+{
+  if (num_dims == 0)
+  {
+    return 0;
+  }
+  size_t offset = 0;
+  for (int idx = 0; idx < num_dims; ++idx)
+  {
+    // if we need to skip this axis
+    bool is_axis = false;
+    if (axis != nullptr)
+    {
+      for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx)
+      {
+        if (idx == axis[axis_idx])
+        {
+          is_axis = true;
+          break;
+        }
+      }
+    }
+    if (!is_axis)
+    {
+      offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]);
+    }
+  }
+  return offset;
+}
+
+// Gets next index to iterate through a multidimensional array.
+inline bool nextIndex(const int32_t num_dims, const int32_t *dims, int32_t *current)
+{
+  if (num_dims == 0)
+  {
+    return false;
+  }
+  int carry = 1;
+  for (int idx = num_dims - 1; idx >= 0; --idx)
+  {
+    int current_val = current[idx] + carry;
+    if (dims[idx] == current_val)
+    {
+      current[idx] = 0;
+    }
+    else
+    {
+      current[idx] = current_val;
+      carry = 0;
+      break;
+    }
+  }
+  return (carry == 0);
+}
+
+// Get common shape dim, assert that they all agree.
+inline int MatchingDim(const core::OMRuntimeShape &shape1, int index1,
+                       const core::OMRuntimeShape &shape2, int index2)
+{
+  assert(shape1.dims(index1) == shape2.dims(index2));
+  return shape1.dims(index1);
+}
+
+// Data is required to be contiguous, and so many operators can use either the
+// full array flat size or the flat size with one dimension skipped (commonly
+// the depth).
+inline int flatSizeSkipDim(const int32_t *dims_data, int skip_dim, int num_dims)
+{
+  int flat_size = 1;
+  for (int i = 0; i < num_dims; ++i)
+  {
+    flat_size *= (i == skip_dim) ? 1 : dims_data[i];
+  }
+  return flat_size;
+}
+
+inline int offset(const int32_t *dims_data, int i0, int i1, int i2, int i3)
+{
+  return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3;
+}
+
+inline int offset(const int32_t *dims_data, int i0, int i1, int i2, int i3, int i4)
+{
+  return (((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3) * dims_data[4] + i4;
+}
+
+template <typename T>
+inline T activationFunctionWithMinMax(T x, T output_activation_min, T output_activation_max)
+{
+  using std::max;
+  using std::min;
+  return min(max(x, output_activation_min), output_activation_max);
+}
+
+} // namespace pal
+} // namespace execute
+} // namespace onert_micro
+
+#endif // ONERT_MICRO_EXECUTE_PAL_UTILS_H

onert-micro/onert-micro/include/pal/mcu/KernelsToBuild.lst

@@ -1 +1,3 @@
 REGISTER_KERNEL(ABS, Abs)
+
+REGISTER_KERNEL(CONCATENATION, Concatenation)