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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/Sub.h"
#include "kernels/Utils.h"
#include "PALSub.h"
#include <tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h>
#include <stdexcept>
namespace luci_interpreter
{
namespace kernels
{
Sub::Sub(const Tensor *input1, const Tensor *input2, Tensor *output, const SubParams ¶ms)
: KernelWithParams<SubParams>({input1, input2}, {output}, params)
{
}
void Sub::configure()
{
LUCI_INTERPRETER_CHECK(!(input1()->element_type() != input2()->element_type()))
LUCI_INTERPRETER_CHECK(!(input1()->element_type() != output()->element_type()))
output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape()));
}
void Sub::execute() const
{
switch (input1()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::S64:
evalInteger<int64_t>();
break;
case DataType::S32:
evalInteger<int32_t>();
break;
case DataType::U8:
evalQuantized();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void Sub::evalFloat() const
{
tflite::ArithmeticParams params{};
fillArithmeticActivationRange<float>(params, _params.activation);
const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
getTensorShape(input1()), getTensorShape(input2()), ¶ms);
if (need_broadcast)
{
tflite::reference_ops::BroadcastSubSlow(
params, getTensorShape(input1()), getTensorData<float>(input1()), getTensorShape(input2()),
getTensorData<float>(input2()), getTensorShape(output()), getTensorData<float>(output()));
}
else
{
luci_interpreter_pal::Sub(params, getTensorShape(input1()), getTensorData<float>(input1()),
getTensorShape(input2()), getTensorData<float>(input2()),
getTensorShape(output()), getTensorData<float>(output()));
}
}
template <typename T> void Sub::evalInteger() const
{
tflite::ArithmeticParams params{};
fillArithmeticActivationRange<T>(params, _params.activation);
const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
getTensorShape(input1()), getTensorShape(input2()), ¶ms);
if (need_broadcast)
{
tflite::reference_ops::BroadcastSubSlow(
params, getTensorShape(input1()), getTensorData<T>(input1()), getTensorShape(input2()),
getTensorData<T>(input2()), getTensorShape(output()), getTensorData<T>(output()));
}
else
{
tflite::reference_ops::Sub(params, getTensorShape(input1()), getTensorData<T>(input1()),
getTensorShape(input2()), getTensorData<T>(input2()),
getTensorShape(output()), getTensorData<T>(output()));
}
}
void Sub::evalQuantized() const
{
const auto input1_scale = static_cast<double>(input1()->scale());
const auto input2_scale = static_cast<double>(input2()->scale());
const auto output_scale = static_cast<double>(output()->scale());
const int left_shift = 20;
const double twice_max_input_scale = 2 * std::max(input1_scale, input2_scale);
const double real_input1_multiplier = input1_scale / twice_max_input_scale;
const double real_input2_multiplier = input2_scale / twice_max_input_scale;
const double real_output_multiplier = twice_max_input_scale / ((1 << left_shift) * output_scale);
int32_t input1_multiplier{}, input2_multiplier{}, output_multiplier{};
int input1_shift{}, input2_shift{}, output_shift{};
quantizeMultiplierSmallerThanOneExp(real_input1_multiplier, &input1_multiplier, &input1_shift);
quantizeMultiplierSmallerThanOneExp(real_input2_multiplier, &input2_multiplier, &input2_shift);
quantizeMultiplierSmallerThanOneExp(real_output_multiplier, &output_multiplier, &output_shift);
int32_t activation_min{};
int32_t activation_max{};
calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
tflite::ArithmeticParams params{};
params.left_shift = left_shift;
// The kernel expects inputs' zero points to be negated.
params.input1_offset = -input1()->zero_point(); // Note the '-'.
params.input1_multiplier = input1_multiplier;
params.input1_shift = input1_shift;
params.input2_offset = -input2()->zero_point(); // Note the '-'.
params.input2_multiplier = input2_multiplier;
params.input2_shift = input2_shift;
params.output_offset = output()->zero_point();
params.output_multiplier = output_multiplier;
params.output_shift = output_shift;
params.quantized_activation_min = activation_min;
params.quantized_activation_max = activation_max;
const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
getTensorShape(input1()), getTensorShape(input2()), ¶ms);
if (need_broadcast)
{
tflite::reference_ops::BroadcastQuantSubSlow(
params, getTensorShape(input1()), getTensorData<uint8_t>(input1()), getTensorShape(input2()),
getTensorData<uint8_t>(input2()), getTensorShape(output()), getTensorData<uint8_t>(output()));
}
else
{
tflite::reference_ops::Sub(params, getTensorShape(input1()), getTensorData<uint8_t>(input1()),
getTensorShape(input2()), getTensorData<uint8_t>(input2()),
getTensorShape(output()), getTensorData<uint8_t>(output()));
}
}
} // namespace kernels
} // namespace luci_interpreter
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