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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 "CustomKernel.h"
namespace onert
{
namespace frontend
{
namespace custom
{
using namespace backend::custom;
class APIConverter
{
public:
static nnfw_operand convertOperand(void *alloc, const TypeInfo &type)
{
nnfw_operand api_operand;
api_operand.allocation = alloc;
api_operand.type = convertType(type);
return api_operand;
}
static nnfw_tensorinfo convertType(const TypeInfo &type)
{
nnfw_tensorinfo api_type;
api_type.rank = type.shape.rank();
assert(type.shape.rank() <= 6);
std::copy(type.shape.dims().begin(), type.shape.dims().end(), std::begin(api_type.dims));
switch (type.dtype)
{
case ir::DataType::FLOAT32:
api_type.dtype = NNFW_TYPE_TENSOR_FLOAT32;
break;
case ir::DataType::INT32:
api_type.dtype = NNFW_TYPE_TENSOR_INT32;
break;
case ir::DataType::QUANT_UINT8_ASYMM:
api_type.dtype = NNFW_TYPE_TENSOR_QUANT8_ASYMM;
break;
case ir::DataType::BOOL8:
api_type.dtype = NNFW_TYPE_TENSOR_BOOL;
break;
default:
throw std::runtime_error("Unsupported tensor datatype");
}
return api_type;
}
};
Kernel::Kernel(const nnfw_custom_eval evalFunction)
: _in_params(), _userdata(nullptr), _userdata_size(0), _evalFunction(evalFunction)
{
}
void Kernel::configure(CustomKernelConfigParams &&inParams)
{
_userdata = inParams.userdata;
_userdata_size = inParams.userdata_size;
_in_params = std::move(inParams);
}
void Kernel::run()
{
nnfw_custom_kernel_params params;
// set input tensor buffer and types
params.ninputs = _in_params.input_tensors.size();
params.inputs = new nnfw_operand[params.ninputs];
for (size_t i = 0; i < params.ninputs; ++i)
{
auto *buf = _in_params.input_tensors[i]->buffer();
assert(buf);
params.inputs[i] = APIConverter::convertOperand(buf, _in_params.input_types[i]);
}
// set output tensor buffer and types
params.noutputs = _in_params.output_tensors.size();
params.outputs = new nnfw_operand[params.noutputs];
for (size_t i = 0; i < params.noutputs; ++i)
{
auto *buf = _in_params.output_tensors[i]->buffer();
assert(buf);
params.outputs[i] = APIConverter::convertOperand(buf, _in_params.output_types[i]);
}
_evalFunction(¶ms, _userdata, _userdata_size);
delete[] params.inputs;
delete[] params.outputs;
}
} // namespace custom
} // namespace frontend
} // namespace onert
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