PALFullyConnected.h

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/*
 * 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_FULLY_CONNECTED_H
#define ONERT_MICRO_EXECUTE_PAL_FULLY_CONNECTED_H
 
#include "PALFullyConnectedCommon.h"
 
#include <arm_nnfunctions.h>
 
namespace onert_micro
{
namespace execute
{
namespace pal
{
template <>
OMStatus FullyConnected<int8_t>(const core::FullyConnectedParams &params, const int8_t *input_data,
                                const core::OMRuntimeShape &filter_shape, const int8_t *filter_data,
                                const int32_t *bias_data, const core::OMRuntimeShape &output_shape,
                                int8_t *output_data)
{
  const int filter_dim_count = filter_shape.dimensionsCount();
  const int output_dim_count = output_shape.dimensionsCount();
  const int batches =
    flatSizeSkipDim(output_shape.dimsData(), output_dim_count - 1, output_dim_count);
  const int output_depth = output_shape.dims(output_dim_count - 1);
  const int accum_depth = filter_shape.dims(filter_dim_count - 1);
 
  cmsis_nn_fc_params fc_params;
  fc_params.input_offset = params.input_offset;
  fc_params.output_offset = params.output_offset;
  fc_params.filter_offset = params.weights_offset;
  fc_params.activation.min = params.quantized_activation_min;
  fc_params.activation.max = params.quantized_activation_max;
 
  cmsis_nn_per_tensor_quant_params quant_params;
  quant_params.multiplier = params.output_multiplier;
  quant_params.shift = params.output_shift;
 
  cmsis_nn_dims input_dims;
  input_dims.n = batches;
  input_dims.h = 1;
  input_dims.w = 1;
  input_dims.c = accum_depth;
 
  cmsis_nn_dims filter_dims;
  filter_dims.n = accum_depth;
  filter_dims.h = 1;
  filter_dims.w = 1;
  filter_dims.c = output_depth;
 
  cmsis_nn_dims bias_dims;
  bias_dims.n = 1;
  bias_dims.h = 1;
  bias_dims.w = 1;
  bias_dims.c = output_depth;
 
  cmsis_nn_dims output_dims;
  output_dims.n = batches;
  output_dims.h = 1;
  output_dims.w = 1;
  output_dims.c = output_depth;
 
  int32_t buf_size = arm_fully_connected_s8_get_buffer_size(&filter_dims);
  auto buffer = std::make_unique<int8_t[]>(buf_size);
  assert(buffer != nullptr);
 
  cmsis_nn_context ctx;
  ctx.buf = buffer.get();
  ctx.size = buf_size;
 
  auto res =
    arm_fully_connected_s8(&ctx, &fc_params, &quant_params, &input_dims, input_data, &filter_dims,
                           filter_data, &bias_dims, bias_data, &output_dims, output_data);
  assert(res == ARM_CMSIS_NN_SUCCESS);
  if (res != ARM_CMSIS_NN_SUCCESS)
    return CmsisNNError;
 
  return Ok;
}
 
template <>
OMStatus FullyConnected(const core::FullyConnectedParams &params, const int16_t *input_data,
                        const core::OMRuntimeShape &filter_shape, const int8_t *filter_data,
                        const int64_t *bias_data, const core::OMRuntimeShape &output_shape,
                        int16_t *output_data)
{
  const int filter_dim_count = filter_shape.dimensionsCount();
  const int output_dim_count = output_shape.dimensionsCount();
  const int batches =
    flatSizeSkipDim(output_shape.dimsData(), output_dim_count - 1, output_dim_count);
  const int output_depth = output_shape.dims(output_dim_count - 1);
  const int accum_depth = filter_shape.dims(filter_dim_count - 1);
 
  cmsis_nn_fc_params fc_params;
  fc_params.input_offset = params.input_offset;
  fc_params.output_offset = params.output_offset;
  fc_params.filter_offset = params.weights_offset;
  fc_params.activation.min = params.quantized_activation_min;
  fc_params.activation.max = params.quantized_activation_max;
 
  cmsis_nn_per_tensor_quant_params quant_params;
  quant_params.multiplier = params.output_multiplier;
  quant_params.shift = params.output_shift;
 
  cmsis_nn_dims input_dims;
  input_dims.n = batches;
  input_dims.h = 1;
  input_dims.w = 1;
  input_dims.c = accum_depth;
 
  cmsis_nn_dims filter_dims;
  filter_dims.n = accum_depth;
  filter_dims.h = 1;
  filter_dims.w = 1;
  filter_dims.c = output_depth;
 
  cmsis_nn_dims bias_dims;
  bias_dims.n = 1;
  bias_dims.h = 1;
  bias_dims.w = 1;
  bias_dims.c = output_depth;
 
  cmsis_nn_dims output_dims;
  output_dims.n = batches;
  output_dims.h = 1;
  output_dims.w = 1;
  output_dims.c = output_depth;
 
  int32_t buf_size = arm_fully_connected_s16_get_buffer_size(&filter_dims);
  auto buffer = std::make_unique<int8_t[]>(buf_size);
  assert(buffer != nullptr);
 
  cmsis_nn_context ctx;
  ctx.buf = buffer.get();
  ctx.size = buf_size;
 
  auto res =
    arm_fully_connected_s16(&ctx, &fc_params, &quant_params, &input_dims, input_data, &filter_dims,
                            filter_data, &bias_dims, bias_data, &output_dims, output_data);
  assert(res == ARM_CMSIS_NN_SUCCESS);
 
  if (res != ARM_CMSIS_NN_SUCCESS)
    return CmsisNNError;
 
  return Ok;
}
 
} // namespace pal
} // namespace execute
} // namespace onert_micro
 
#endif // ONERT_MICRO_EXECUTE_PAL_FULLY_CONNECTED_COMMON_H