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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2019 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/LogSoftmax.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/reference/log_softmax.h>
#include "PALLogSoftmax.h"
namespace luci_interpreter
{
namespace kernels
{
LogSoftmax::LogSoftmax(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {}
void LogSoftmax::configure()
{
LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type());
if (input()->element_type() == DataType::U8)
{
LUCI_INTERPRETER_CHECK(output()->scale() == 16. / 256);
LUCI_INTERPRETER_CHECK(output()->zero_point() == 255);
tflite::SoftmaxParams params{};
params.table = _table;
params.beta = 1.0;
luci_interpreter_pal::PopulateSoftmaxLookupTable(¶ms, input()->scale(), params.beta);
}
// TODO: enable it only if kernel with dynamic shapes
output()->resize(input()->shape());
}
void LogSoftmax::execute() const
{
switch (input()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::U8:
evalQuantized();
break;
default:
assert(false && "Unsupported type.");
}
}
void LogSoftmax::evalFloat() const
{
tflite::SoftmaxParams params{};
tflite::reference_ops::LogSoftmax(params, getTensorShape(input()), getTensorData<float>(input()),
getTensorShape(output()), getTensorData<float>(output()));
}
void LogSoftmax::evalQuantized() const
{
const auto input_shape = getTensorShape(input());
const auto output_shape = getTensorShape(output());
const auto input_scale = input()->scale();
uint8_t *output_data = getTensorData<uint8_t>(output());
const uint8_t *input_data = getTensorData<uint8_t>(input());
const float beta = 1.0;
tflite::SoftmaxParams params{};
params.table = const_cast<float *>(_table);
params.zero_point = output()->zero_point();
params.scale = output()->scale();
luci_interpreter_pal::InitializeParams(¶ms, input_scale, beta);
luci_interpreter_pal::LogSoftmax(params, input_scale, input_shape, input_data, output_shape,
output_data);
}
} // namespace kernels
} // namespace luci_interpreter
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