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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/core/NEON/kernels/NEBinaryLogicalOperationKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/NEON/NEElementwiseOperationFuncs.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
#include <algorithm>
#include <arm_neon.h>
#include <map>
#include <string>
namespace arm_compute
{
class Coordinates;
} // namespace arm_compute
namespace arm_compute
{
template <BinaryLogicalOperation op, typename ScalarType>
inline ScalarType elementwise_logic_op_scalar(const ScalarType &a, const ScalarType &b)
{
auto res = ScalarType(0);
switch (op)
{
case BinaryLogicalOperation::AND:
res = a & b;
break;
case BinaryLogicalOperation::OR:
res = a | b;
break;
default:
ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
}
return res;
}
template <BinaryLogicalOperation op, typename VectorType>
inline VectorType elementwise_logic_op(const VectorType &a, const VectorType &b)
{
VectorType res = {0, 0, 0, 0};
switch (op)
{
case BinaryLogicalOperation::AND:
res = wrapper::vand(a, b);
break;
case BinaryLogicalOperation::OR:
res = wrapper::vorr(a, b);
break;
default:
ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
}
return res;
}
template <BinaryLogicalOperation op>
inline uint8x16x4_t elementwise_logic_op(const uint8x16x4_t &a, const uint8x16x4_t &b)
{
uint8x16x4_t out = {{
elementwise_logic_op<op>(a.val[0], b.val[0]), elementwise_logic_op<op>(a.val[1], b.val[1]),
elementwise_logic_op<op>(a.val[2], b.val[2]), elementwise_logic_op<op>(a.val[3], b.val[3]),
}};
return out;
}
template <BinaryLogicalOperation op, typename ScalarType, typename VectorType>
inline VectorType elementwise_logic_op_broadcast(const VectorType &a,
const ScalarType &broadcast_value,
const bool reorder)
{
VectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag());
return elementwise_logic_op<op>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector);
}
template <BinaryLogicalOperation op, typename ScalarType, typename VectorType>
inline int elementwise_logic_op_loop(int window_start_x, int window_end_x, int window_step_x,
const ScalarType *input1_ptr, const ScalarType *input2_ptr,
ScalarType *output_ptr)
{
int x = window_start_x;
for (; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto a = wrapper::vloadq(input1_ptr + x);
const auto b = wrapper::vloadq(input2_ptr + x);
wrapper::vstore(output_ptr + x, elementwise_logic_op<op>(a, b));
}
return x;
}
template <BinaryLogicalOperation op, typename ScalarType, typename VectorType>
inline int elementwise_logic_op_broadcast_loop(int window_start_x, int window_end_x,
int window_step_x,
const ScalarType *non_broadcast_input_ptr,
const ScalarType &broadcast_value,
ScalarType *output_ptr, const bool reorder)
{
int x = window_start_x;
for (; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto a = wrapper::vloadq((non_broadcast_input_ptr + x));
wrapper::vstore(output_ptr + x,
elementwise_logic_op_broadcast<op>(a, broadcast_value, reorder));
}
return x;
}
template <BinaryLogicalOperation op, typename ScalarType, typename VectorType>
void elementwise_logic_op(const ITensor *in1, const ITensor *in2, ITensor *out,
const Window &window)
{
elementwise_op(in1, in2, out, window, &elementwise_logic_op_scalar<op, ScalarType>,
&elementwise_logic_op_broadcast_loop<op, ScalarType, VectorType>,
&elementwise_logic_op_loop<op, ScalarType, VectorType>);
}
std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)> configure_func(
const ITensor *input1, const ITensor *input2, ITensor *output,
std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function)
{
std::string function_to_call("op_");
function_to_call += string_from_data_type(input1->info()->data_type()) + "_";
function_to_call += string_from_data_type(input2->info()->data_type()) + "_";
function_to_call += string_from_data_type(output->info()->data_type());
auto it = map_function.find(function_to_call);
if (it != map_function.end())
{
auto func = it->second;
return [func](const ITensor *input1, const ITensor *input2, ITensor *output,
const Window &window) { func(input1, input2, output, window); };
}
return nullptr;
}
template <BinaryLogicalOperation op>
std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)>
configure_logic_func(const ITensor *input1, const ITensor *input2, ITensor *output)
{
static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function = {
{"op_U8_U8_U8", &elementwise_logic_op<op, uint8_t, uint8x16_t>},
{"op_QASYMM8_QASYMM8_QASYMM8", &elementwise_logic_op<op, uint8_t, uint8x16_t>}};
return configure_func(input1, input2, output, map_function);
}
void NEBinaryLogicalOperationKernel::configure(BinaryLogicalOperation op, const ITensor *input1,
const ITensor *input2, ITensor *output)
{
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info()));
configure_common(input1, input2, output);
switch (op)
{
case BinaryLogicalOperation::AND:
_function = configure_logic_func<BinaryLogicalOperation::AND>(input1, input2, output);
break;
case BinaryLogicalOperation::OR:
_function = configure_logic_func<BinaryLogicalOperation::OR>(input1, input2, output);
break;
default:
ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
}
}
Status NEBinaryLogicalOperationKernel::validate_arguments(const ITensorInfo &input1,
const ITensorInfo &input2,
const ITensorInfo &output)
{
// Validate in case of configured output
if (output.total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8,
DataType::QASYMM8);
}
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
const TensorShape out_shape =
TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0,
"Inputs are not broadcast compatible");
// Validate in case of configured output
if (output.total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(
detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
"Wrong shape for output");
}
return Status{};
}
Status NEBinaryLogicalOperationKernel::validate(BinaryLogicalOperation op,
const ITensorInfo *input1,
const ITensorInfo *input2,
const ITensorInfo *output)
{
ARM_COMPUTE_UNUSED(op);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output));
return Status{};
}
} // namespace arm_compute
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