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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 "luci/Import/Nodes/CircleConst.h"
#include <luci/IR/Nodes/CircleConst.h>
#include <luci/Log.h>
#include <loco.h>
#include <oops/UserExn.h>
#include <cassert>
#include <ostream>
#include <string>
#include <vector>
namespace
{
std::ostream &operator<<(std::ostream &os, const luci::VectorWrapper<int32_t> &vect)
{
uint32_t seq = 0;
for (const auto &v : vect)
{
if (seq)
os << ", ";
os << v;
seq++;
}
return os;
}
using namespace luci;
template <loco::DataType DT>
void copy_data(const VectorWrapper<uint8_t> &raw_data, uint32_t num_elements,
CircleConst *const_node)
{
using T = typename loco::DataTypeImpl<DT>::Type;
// TODO calculate the exact buffer size of sparse tensor
if (const_node->sparsityparam())
{
num_elements = raw_data.size() / sizeof(T);
}
assert(raw_data.size() == num_elements * sizeof(T));
const auto *data = reinterpret_cast<const T *>(raw_data.data());
const_node->size<DT>(num_elements);
for (uint32_t i = 0; i < num_elements; ++i)
{
const_node->at<DT>(i) = data[i];
}
}
template <>
void copy_data<loco::DataType::STRING>(const VectorWrapper<uint8_t> &raw_data,
uint32_t num_elements, CircleConst *const_node)
{
assert(const_node->sparsityparam() == nullptr);
const auto *data = reinterpret_cast<const char *>(raw_data.data());
const auto *i32d = reinterpret_cast<const int32_t *>(raw_data.data());
// de-serialize string data
// int32_t count
// int32_t offsets[count + 1]
// string values[count]
assert(static_cast<uint32_t>(*i32d) == num_elements);
i32d++; // skip count
std::vector<int32_t> offsets;
offsets.push_back(*i32d++);
for (uint32_t i = 0; i < num_elements; ++i)
{
offsets.push_back(*i32d++);
}
assert(offsets.size() == num_elements + 1);
const_node->size<loco::DataType::STRING>(num_elements);
for (uint32_t i = 0; i < num_elements; ++i)
{
int32_t start = offsets[i];
int32_t next = offsets[i + 1];
std::string value(data + start, next - start);
const_node->at<loco::DataType::STRING>(i) = value;
}
}
} // namespace
namespace luci
{
CircleNode *CircleConstNodeBuilder::build(TensorIndex tensor_index,
GraphBuilderContext *context) const
{
assert(tensor_index >= 0);
LOGGER(l);
auto graph = context->graph();
auto reader = context->reader();
const auto tensors = reader->tensors();
const auto const_tensor = tensors[tensor_index];
assert(const_tensor != nullptr);
if (const_tensor->is_variable())
{
// Create CircleVariable for variable
return nullptr;
}
assert(reader->buffers()[const_tensor->buffer()] != nullptr);
const auto buffer = wrap(reader->buffers()[const_tensor->buffer()]->data());
const auto const_dims = wrap(const_tensor->shape()); // in NHWC
if (const_dims.size() == 0 && buffer.empty())
{
// unknown shape tensor and scalar tensor
return nullptr;
}
// if tensor_index is used as output to some other operator, this is not a constant
auto tensoroutputs = context->tensoroutputs();
if (tensoroutputs->find(tensor_index))
{
// other operator output tensor
return nullptr;
}
uint32_t num_elements = 1;
for (uint32_t r = 0; r < const_dims.size(); ++r)
{
num_elements = num_elements * const_dims[r];
}
if (buffer.empty() && num_elements > 0)
{
// normal empty tensor
return nullptr;
}
auto const_node = graph->nodes()->create<CircleConst>();
copy_tensor_attributes(const_tensor, const_node);
const_node->shape_status(luci::ShapeStatus::VALID);
INFO(l) << "[luci] NodeFinder const_node(" << tensor_index << ") -> " << const_node << " "
<< const_dims << std::endl;
if (num_elements > 0)
{
switch (luci_datatype(const_tensor->type()))
{
case loco::DataType::FLOAT32:
copy_data<loco::DataType::FLOAT32>(buffer, num_elements, const_node);
break;
case loco::DataType::FLOAT16:
copy_data<loco::DataType::FLOAT16>(buffer, num_elements, const_node);
break;
case loco::DataType::U8:
copy_data<loco::DataType::U8>(buffer, num_elements, const_node);
break;
case loco::DataType::S8:
copy_data<loco::DataType::S8>(buffer, num_elements, const_node);
break;
case loco::DataType::S16:
copy_data<loco::DataType::S16>(buffer, num_elements, const_node);
break;
case loco::DataType::S32:
copy_data<loco::DataType::S32>(buffer, num_elements, const_node);
break;
case loco::DataType::S64:
copy_data<loco::DataType::S64>(buffer, num_elements, const_node);
break;
case loco::DataType::BOOL:
copy_data<loco::DataType::BOOL>(buffer, num_elements, const_node);
break;
case loco::DataType::STRING:
copy_data<loco::DataType::STRING>(buffer, num_elements, const_node);
break;
default:
throw oops::UserExn("Unsupported tensor type",
circle::EnumNameTensorType(const_tensor->type()));
}
}
return const_node;
}
} // namespace luci
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