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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/Concatenation.h>
#include "OperationUtil.h"
#include "exec/interp/Registration.h"
#include "model/operation/ConcatNode.h"
#include "misc/polymorphic_downcast.h"
namespace neurun
{
namespace exec
{
namespace interp
{
namespace concat
{
void prepareConcat(ExecEnv *env, const model::Operation &node)
{
const auto &concat_node =
nnfw::misc::polymorphic_downcast<const model::operation::ConcatNode &>(node);
const auto first_index = node.getInputs().at(0);
const auto out_index = node.getOutputs().at(0);
const auto first_tensor = env->tensorAt(first_index);
uint32_t out_axis_dimension = 0;
const int32_t axis_raw = concat_node.param().axis;
const uint32_t axis = (axis_raw < 0) ? (axis_raw + first_tensor->num_dimensions()) : axis_raw;
// All inputs shape should be same except axis dimension
// All inputs type should be same
for (auto input : node.getInputs())
{
assert(first_tensor->num_dimensions() == env->tensorAt(input)->num_dimensions());
assert(first_tensor->data_type() == env->tensorAt(input)->data_type());
for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++)
{
if (i == axis)
{
out_axis_dimension += env->tensorAt(input)->dimension(i);
continue;
}
assert(first_tensor->dimension(i) == env->tensorAt(input)->dimension(i));
}
}
// Make output tensor info using first input tensor info, and accumulated axis dimension value
auto out_shape = first_tensor->tensorInfo().shape();
out_shape.dim(axis) = out_axis_dimension;
env->allocateIfNeeded(out_index,
model::OperandInfo{out_shape, first_tensor->tensorInfo().typeInfo()});
auto out_tensor = env->tensorAt(out_index);
UNUSED_RELEASE(out_tensor);
// Output shape should be same with input except axis dimension
// Output type should be same with input
assert(first_tensor->data_type() == out_tensor->data_type());
for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++)
{
if (i == axis)
{
continue;
}
assert(first_tensor->dimension(i) == out_tensor->dimension(i));
}
}
void invoke(const std::vector<const ITensor *> in_tensors, const ITensor *out_tensor, uint32_t axis)
{
const uint32_t count = in_tensors.size();
// Calculate
nnfw::cker::ConcatenationParams cker_param;
cker_param.axis = (int8_t)axis;
cker_param.inputs_count = count;
const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
std::vector<nnfw::cker::Shape> in_shapes;
std::vector<const nnfw::cker::Shape *> in_shape_ptrs;
in_shapes.reserve(count);
in_shape_ptrs.reserve(count);
std::vector<const float *> in_ptrs;
for (uint32_t i = 0; i < count; i++)
{
in_shapes.push_back(convertShape(in_tensors[i]->tensorInfo().shape()));
in_shape_ptrs.push_back(&in_shapes[i]);
in_ptrs.push_back(reinterpret_cast<const float *>(in_tensors[i]->bufferRO()));
}
auto out_buffer = out_tensor->buffer();
float *out_ptr = reinterpret_cast<float *>(out_buffer);
nnfw::cker::Concatenation<float>(cker_param, in_shape_ptrs.data(), in_ptrs.data(), out_shape,
out_ptr);
}
void invokeConcat(const ExecEnv *env, const model::Operation &node)
{
const auto &concat_node =
nnfw::misc::polymorphic_downcast<const model::operation::ConcatNode &>(node);
const int32_t axis_raw = concat_node.param().axis;
std::vector<const ITensor *> in_tensors;
for (const auto &e : concat_node.getInputs())
{
in_tensors.emplace_back(env->tensorAt(e));
}
const auto out_index = node.getOutputs().at(0);
const auto out_tensor = env->tensorAt(out_index);
const uint32_t axis = (axis_raw < 0) ? (axis_raw + out_tensor->num_dimensions()) : axis_raw;
const auto data_type = in_tensors[0]->data_type();
if (data_type == model::DataType::FLOAT32)
{
invoke(in_tensors, out_tensor, axis);
}
else
{
throw std::runtime_error{"NYI: Support float32 only"};
}
}
} // namespace concat
OpKernel *getConcatNode()
{
static OpKernel kernel = {concat::prepareConcat, concat::invokeConcat};
return &kernel;
}
} // namespace interp
} // namespace exec
} // namespace neurun
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