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/*
* Copyright (c) 2019 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 __NNFW_CKER_BINARY_ARITHMETIC_OPS_H__
#define __NNFW_CKER_BINARY_ARITHMETIC_OPS_H__
#include <functional>
#include "cker/operation/optimized/BinaryArithmeticOps.h"
#include "cker/operation/reference/BinaryArithmeticOps.h"
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
namespace nnfw
{
namespace cker
{
namespace
{
template <typename T>
const std::function<T(const T &, const T &)> GetBinaryArtithmeticFn(BinaryArithmeticOpType type)
{
switch (type)
{
case BinaryArithmeticOpType::ADD:
{
return [](const T &a, const T &b) -> T { return a + b; };
}
case BinaryArithmeticOpType::MUL:
{
return [](const T &a, const T &b) -> T { return a * b; };
}
case BinaryArithmeticOpType::SUB:
{
return [](const T &a, const T &b) -> T { return a - b; };
}
case BinaryArithmeticOpType::DIV:
{
return [](const T &a, const T &b) -> T {
if (b == 0)
{
throw std::runtime_error("Divide by zero");
}
return a / b;
};
}
default:
{
assert(false);
return nullptr;
}
}
}
} // namespace
template <typename T>
inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape,
const T *input1_data, const Shape &input2_shape,
const T *input2_data, const Shape &output_shape, T *output_data)
{
reference::BinaryArithmeticOp(params, input1_shape, input1_data, input2_shape, input2_data,
output_shape, output_data, GetBinaryArtithmeticFn<T>(params.type));
}
template <>
inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape,
const float *input1_data, const Shape &input2_shape,
const float *input2_data, const Shape &output_shape,
float *output_data)
{
// Supported type is only float now
switch (params.type)
{
case nnfw::cker::BinaryArithmeticOpType::ADD:
optimized::Add(params, input1_shape, input1_data, input2_shape, input2_data, output_shape,
output_data);
break;
case nnfw::cker::BinaryArithmeticOpType::MUL:
optimized::Mul(params, input1_shape, input1_data, input2_shape, input2_data, output_shape,
output_data);
break;
case nnfw::cker::BinaryArithmeticOpType::SUB:
optimized::Sub(params, input1_shape, input1_data, input2_shape, input2_data, output_shape,
output_data);
break;
case nnfw::cker::BinaryArithmeticOpType::DIV:
reference::BinaryArithmeticOp(params, input1_shape, input1_data, input2_shape, input2_data,
output_shape, output_data,
GetBinaryArtithmeticFn<float>(params.type));
break;
default:
assert(false);
break;
}
}
template <typename T>
inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam ¶ms,
const Shape &input1_shape, const T *input1_data,
const Shape &input2_shape, const T *input2_data,
const Shape &output_shape, T *output_data)
{
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2);
const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape);
const auto fn = GetBinaryArtithmeticFn<T>(params.type);
// Comment from tensorflow lite:
//
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b)
{
for (int y = 0; y < extended_output_shape.Dims(1); ++y)
{
for (int x = 0; x < extended_output_shape.Dims(2); ++x)
{
for (int c = 0; c < extended_output_shape.Dims(3); ++c)
{
output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax(
fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
params.quantized_activation_min, params.quantized_activation_max);
}
}
}
}
}
template <>
inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam ¶ms,
const Shape &input1_shape, const float *input1_data,
const Shape &input2_shape, const float *input2_data,
const Shape &output_shape, float *output_data)
{
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2);
const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape);
const auto fn = GetBinaryArtithmeticFn<float>(params.type);
for (int b = 0; b < extended_output_shape.Dims(0); ++b)
{
for (int y = 0; y < extended_output_shape.Dims(1); ++y)
{
for (int x = 0; x < extended_output_shape.Dims(2); ++x)
{
for (int c = 0; c < extended_output_shape.Dims(3); ++c)
{
output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax(
fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
input2_data[SubscriptToIndex(desc2, b, y, x, c)]),
params.float_activation_min, params.float_activation_max);
}
}
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_BINARY_ARITHMETIC_OPS_H__
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