Gather.cpp

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/*
 * Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved
 * Copyright 2021 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.
 */
 
#include "kernels/Gather.h"
#include "kernels/Utils.h"
#include "PALGather.h"
 
#include <stdexcept>
#include <cassert>
 
namespace luci_interpreter
{
 
namespace kernels
{
 
Gather::Gather(const Tensor *params, const Tensor *indices, Tensor *output,
               const GatherParams &gparams)
  : KernelWithParams<GatherParams>({params, indices}, {output}, gparams)
{
}
 
void Gather::configure()
{
  if (params()->element_type() == DataType::FLOAT32)
  {
    LUCI_INTERPRETER_CHECK(output()->element_type() == DataType::FLOAT32);
  }
  else
  {
    throw std::runtime_error("luci-intp Gather(1) Unsupported type.");
  }
 
  LUCI_INTERPRETER_CHECK(indices()->element_type() == DataType::S32 ||
                         indices()->element_type() == DataType::S64);
 
  // refer tensorflow/lite/kernels/gather.cc
 
  const Shape &params_shape = params()->shape();
  const Shape &indices_shape = indices()->shape();
 
  int axis = _params.axis;
  if (axis < 0)
  {
    axis += params_shape.num_dims();
  }
  LUCI_INTERPRETER_CHECK(0 <= axis && axis < params_shape.num_dims());
 
  int batch_dims = _params.batch_dims;
  // batch_dims should be in range: [-rank(indices), rank(indices)].
  // Negative batch_dims is added with rank of positions.
  if (batch_dims < 0)
  {
    batch_dims += indices_shape.num_dims();
  }
  LUCI_INTERPRETER_CHECK(batch_dims <= axis);
  LUCI_INTERPRETER_CHECK(0 <= batch_dims && batch_dims < params_shape.num_dims());
  LUCI_INTERPRETER_CHECK(batch_dims <= indices_shape.num_dims());
  for (int i = 0; i < batch_dims; ++i)
  {
    LUCI_INTERPRETER_CHECK(params_shape.dim(i) == indices_shape.dim(i));
  }
 
  const int num_dimensions = params_shape.num_dims() + indices_shape.num_dims() - 1 - batch_dims;
 
  Shape output_shape(num_dimensions);
  int output_index = 0;
  for (int i = 0; i < axis; ++i)
  {
    output_shape.dim(output_index++) = params_shape.dim(i);
  }
  for (int i = batch_dims; i < indices_shape.num_dims(); ++i)
  {
    output_shape.dim(output_index++) = indices_shape.dim(i);
  }
  for (int i = axis + 1; i < params_shape.num_dims(); ++i)
  {
    output_shape.dim(output_index++) = params_shape.dim(i);
  }
  output()->resize(output_shape);
}
 
void Gather::execute() const
{
  switch (params()->element_type())
  {
    case DataType::FLOAT32:
      evalFloat();
      break;
    default:
      throw std::runtime_error("luci-intp Gather(2) Unsupported type.");
  }
}
 
void Gather::evalFloat() const
{
  assert(indices()->element_type() == DataType::S32 || indices()->element_type() == DataType::S64);
 
  const auto params_data = getTensorData<float>(params());
  auto output_data = getTensorData<float>(output());
 
  tflite::GatherParams tparams;
  tparams.axis = _params.axis;
  tparams.batch_dims = _params.batch_dims;
 
  if (indices()->element_type() == DataType::S32)
  {
    const auto indices_data = getTensorData<int32_t>(indices());
 
    luci_interpreter_pal::Gather<float, int32_t>(tparams, getTensorShape(params()), params_data,
                                                 getTensorShape(indices()), indices_data,
                                                 getTensorShape(output()), output_data);
  }
  else
  {
    const auto indices_data = getTensorData<int64_t>(indices());
 
    luci_interpreter_pal::Gather<float, int64_t>(tparams, getTensorShape(params()), params_data,
                                                 getTensorShape(indices()), indices_data,
                                                 getTensorShape(output()), output_data);
  }
}
 
} // namespace kernels
} // namespace luci_interpreter