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/*
* Copyright (c) 2020 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 <cmath>
#include "OperationUtil.h"
#include "exec/interp/Registration.h"
#include "ir/operation/ReLU.h"
#include "ir/operation/ReLU1.h"
#include "ir/operation/ReLU6.h"
#include "ir/operation/Tanh.h"
namespace neurun
{
namespace exec
{
namespace interp
{
namespace
{
enum class ActivationType
{
ReLU,
ReLU1,
ReLU6,
Tanh
};
void prepare(ExecEnv *env, const ir::Operation &node)
{
const auto input_index = node.getInputs().at(0);
const auto output_index = node.getOutputs().at(0);
const auto input_tensor = env->tensorAt(input_index);
const auto output_info = env->graph().operands().at(output_index).info();
if (output_info.total_size() == 0)
{
// Output's shape and type is same with input
auto input_info = input_tensor->tensorInfo();
// We can handle already allocated (ex. model output)
env->allocateIfNeeded(output_index, input_info);
}
else
{
env->allocateIfNeeded(output_index, output_info);
}
const auto output_tensor = env->tensorAt(output_index);
// Check shape and type lhs is same with output
// TODO Util function to compare TensorInfo
if (input_tensor->data_type() != output_tensor->data_type())
{
throw std::runtime_error{"Interp(Activations): Invalid output type"};
}
}
template <ActivationType act_type>
void evalFloat(const float *input_ptr, float *output_ptr, uint64_t num_elements)
{
std::function<float(const float &)> fn = [](const float &) { return std::nanf(""); };
switch (act_type)
{
case ActivationType::ReLU:
fn = [](const float &in) { return std::max(0.f, in); };
break;
case ActivationType::ReLU1:
fn = [](const float &in) { return std::min(std::max(-1.f, in), 1.f); };
break;
case ActivationType::ReLU6:
fn = [](const float &in) { return std::min(std::max(0.f, in), 6.f); };
break;
case ActivationType::Tanh:
fn = [](const float &in) { return std::tanh(in); };
break;
default:
throw std::runtime_error{"Interp(Activations): NYI - Unsupported activation"};
break;
}
const float *input_end = input_ptr + num_elements;
for (; input_ptr < input_end; input_ptr++, output_ptr++)
{
*output_ptr = fn(*input_ptr);
}
}
template <ActivationType act_type> void invoke(const ExecEnv *env, const ir::Operation &node)
{
const auto input_index = node.getInputs().at(0);
const auto output_index = node.getOutputs().at(0);
// Check lhs shape is same with rhs (with broadcast)
const auto input_tensor = env->tensorAt(input_index);
const auto output_tensor = env->tensorAt(output_index);
const auto data_type = input_tensor->data_type();
if (data_type == ir::DataType::FLOAT32)
{
uint64_t elements = input_tensor->num_elements();
const float *input_start = reinterpret_cast<const float *>(input_tensor->bufferRO());
float *out = reinterpret_cast<float *>(output_tensor->buffer());
evalFloat<act_type>(input_start, out, elements);
}
else
{
throw std::runtime_error{"Interp(ReLU6): NYI - Support float only"};
}
}
} // namespace
OpKernel *getReLU()
{
static OpKernel kernel = {prepare, invoke<ActivationType::ReLU>};
return &kernel;
}
OpKernel *getReLU1()
{
static OpKernel kernel = {prepare, invoke<ActivationType::ReLU1>};
return &kernel;
}
OpKernel *getReLU6()
{
static OpKernel kernel = {prepare, invoke<ActivationType::ReLU6>};
return &kernel;
}
OpKernel *getTanh()
{
static OpKernel kernel = {prepare, invoke<ActivationType::Tanh>};
return &kernel;
}
} // namespace interp
} // namespace exec
} // namespace neurun
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