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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. 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 <cker/operation/BinaryArithmeticOps.h>
#include "OperationUtil.h"
#include "interp/Registration.h"
#include "ir/operation/Add.h"
#include "ir/operation/Sub.h"
#include "ir/operation/Mul.h"
#include "misc/polymorphic_downcast.h"
namespace onert
{
namespace interp
{
namespace
{
enum class OpType
{
ADD,
SUB,
MUL
};
template <typename node_type> void prepareAdd(ExecEnv *env, const ir::Operation &node)
{
const auto &add_node = nnfw::misc::polymorphic_downcast<const node_type &>(node);
const auto lhs_index = node.getInputs().at(add_node.LHS);
const auto rhs_index = node.getInputs().at(add_node.RHS);
const auto out_index = node.getOutputs().at(0);
const auto lhs_tensor = env->tensorAt(lhs_index);
const auto rhs_tensor = env->tensorAt(rhs_index);
// Check shape and type lhs is same with rhs
// TODO Util function to compare TensorInfo
if (lhs_tensor->data_type() != rhs_tensor->data_type())
{
throw std::runtime_error{"Interp(Add): Different input types"};
}
bool try_broadcast = (lhs_tensor->tensorInfo().shape() != rhs_tensor->tensorInfo().shape());
if (try_broadcast)
{
bool success = true;
auto out_shape = calcBroadcastShape(lhs_tensor->tensorInfo().shape(),
rhs_tensor->tensorInfo().shape(), success);
if (!success)
{
throw std::runtime_error{"Interp(Add): Fail to brodcasting"};
}
auto output_info = ir::OperandInfo(out_shape, lhs_tensor->tensorInfo().typeInfo());
// We can handle already allocated (ex. model output)
env->allocateIfNeeded(out_index, output_info);
}
else
{
// Output's shape and type is same with input
auto output_info = lhs_tensor->tensorInfo();
// We can handle already allocated (ex. model output)
env->allocateIfNeeded(out_index, output_info);
}
auto out_tensor = env->tensorAt(out_index);
// Check shape and type lhs is same with output
// TODO Util function to compare TensorInfo
if (lhs_tensor->data_type() != out_tensor->data_type())
{
throw std::runtime_error{"Interp(Add): Invalid output type"};
}
}
inline void setActivationParams(float min, float max, nnfw::cker::BinaryArithmeticOpParam *params)
{
params->float_activation_min = min;
params->float_activation_max = max;
}
inline void setActivationParams(int32_t min, int32_t max,
nnfw::cker::BinaryArithmeticOpParam *params)
{
params->quantized_activation_min = min;
params->quantized_activation_max = max;
}
template <typename raw_type, typename param_type, OpType op_type>
void invoke(const ITensor *lhs_tensor, const ITensor *rhs_tensor, const ITensor *out_tensor,
const param_type ¶m)
{
const auto lhs_buffer = lhs_tensor->bufferRO();
const auto rhs_buffer = rhs_tensor->bufferRO();
auto out_buffer = out_tensor->buffer();
nnfw::cker::BinaryArithmeticOpParam cker_param;
raw_type activation_min, activation_max;
calculateActivationRange(param.activation, &activation_min, &activation_max);
setActivationParams(activation_min, activation_max, &cker_param);
const raw_type *lhs_ptr = reinterpret_cast<const raw_type *>(lhs_buffer);
const raw_type *rhs_ptr = reinterpret_cast<const raw_type *>(rhs_buffer);
raw_type *out_ptr = reinterpret_cast<raw_type *>(out_buffer);
cker_param.type = (op_type == OpType::ADD)
? nnfw::cker::BinaryArithmeticOpType::ADD
: ((op_type == OpType::SUB) ? nnfw::cker::BinaryArithmeticOpType::SUB
: nnfw::cker::BinaryArithmeticOpType::MUL);
if (lhs_tensor->tensorInfo().shape() != rhs_tensor->tensorInfo().shape())
{
const auto lhs_shape = convertExtendShape(lhs_tensor->tensorInfo().shape());
const auto rhs_shape = convertExtendShape(rhs_tensor->tensorInfo().shape());
const auto out_shape = convertExtendShape(out_tensor->tensorInfo().shape());
nnfw::cker::BroadcastBinaryArithmeticOpSlow(cker_param, lhs_shape, lhs_ptr, rhs_shape, rhs_ptr,
out_shape, out_ptr);
return;
}
const auto lhs_shape = convertShape(lhs_tensor->tensorInfo().shape());
const auto rhs_shape = convertShape(rhs_tensor->tensorInfo().shape());
const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
nnfw::cker::BinaryArithmeticOp(cker_param, lhs_shape, lhs_ptr, rhs_shape, rhs_ptr, out_shape,
out_ptr);
}
template <typename node_type, typename param_type, OpType op_type>
void invokeAdd(const ExecEnv *env, const ir::Operation &node)
{
const auto &arithmetic_node = nnfw::misc::polymorphic_downcast<const node_type &>(node);
const auto lhs_index = node.getInputs().at(arithmetic_node.LHS);
const auto rhs_index = node.getInputs().at(arithmetic_node.RHS);
const auto out_index = node.getOutputs().at(0);
const auto lhs_tensor = env->tensorAt(lhs_index);
const auto rhs_tensor = env->tensorAt(rhs_index);
const auto out_tensor = env->tensorAt(out_index);
const auto data_type = lhs_tensor->data_type();
if (data_type == ir::DataType::INT32)
{
invoke<int32_t, param_type, op_type>(lhs_tensor, rhs_tensor, out_tensor,
arithmetic_node.param());
}
else if (data_type == ir::DataType::FLOAT32)
{
invoke<float, param_type, op_type>(lhs_tensor, rhs_tensor, out_tensor, arithmetic_node.param());
}
else
{
throw std::runtime_error{"NYI: Unsupported data type"};
}
}
} // namespace
OpKernel *getAdd()
{
static OpKernel kernel = {prepareAdd<ir::operation::Add>,
invokeAdd<ir::operation::Add, ir::operation::Add::Param, OpType::ADD>};
return &kernel;
}
OpKernel *getSub()
{
static OpKernel kernel = {prepareAdd<ir::operation::Sub>,
invokeAdd<ir::operation::Sub, ir::operation::Sub::Param, OpType::SUB>};
return &kernel;
}
OpKernel *getMul()
{
static OpKernel kernel = {prepareAdd<ir::operation::Mul>,
invokeAdd<ir::operation::Mul, ir::operation::Mul::Param, OpType::MUL>};
return &kernel;
}
} // namespace interp
} // namespace onert
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