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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "tensor_attribute_saver.h"
#include "dense_tensor_store.h"
#include "nearest_neighbor_index_saver.h"
#include "tensor_attribute_constants.h"
#include <vespa/searchlib/util/bufferwriter.h>
#include <vespa/searchlib/attribute/iattributesavetarget.h>
#include <cassert>
using vespalib::GenerationHandler;
namespace search::tensor {
TensorAttributeSaver::TensorAttributeSaver(GenerationHandler::Guard &&guard,
const attribute::AttributeHeader &header,
attribute::EntryRefVector&& refs,
const TensorStore &tensor_store,
IndexSaverUP index_saver)
: AttributeSaver(std::move(guard), header),
_refs(std::move(refs)),
_tensor_store(tensor_store),
_index_saver(std::move(index_saver))
{
}
TensorAttributeSaver::~TensorAttributeSaver() = default;
vespalib::string
TensorAttributeSaver::index_file_suffix()
{
return "nnidx";
}
bool
TensorAttributeSaver::onSave(IAttributeSaveTarget &saveTarget)
{
if (_index_saver) {
if (!saveTarget.setup_writer(index_file_suffix(), "Binary data file for nearest neighbor index")) {
return false;
}
}
auto dat_writer = saveTarget.datWriter().allocBufferWriter();
auto dense_tensor_store = _tensor_store.as_dense();
if (dense_tensor_store != nullptr) {
save_dense_tensor_store(*dat_writer, *dense_tensor_store);
} else {
save_tensor_store(*dat_writer);
}
if (_index_saver) {
auto index_writer = saveTarget.get_writer(index_file_suffix()).allocBufferWriter();
// Note: Implementation of save() is responsible to call BufferWriter::flush().
_index_saver->save(*index_writer);
}
return true;
}
void
TensorAttributeSaver::save_tensor_store(BufferWriter& writer) const
{
assert(get_header_version() == TENSOR_ATTRIBUTE_VERSION);
const uint32_t docid_limit(_refs.size());
vespalib::nbostream stream;
for (uint32_t lid = 0; lid < docid_limit; ++lid) {
if (_tensor_store.encode_stored_tensor(_refs[lid], stream)) {
uint32_t sz = stream.size();
writer.write(&sz, sizeof(sz));
writer.write(stream.peek(), stream.size());
stream.clear();
} else {
uint32_t sz = 0;
writer.write(&sz, sizeof(sz));
}
}
writer.flush();
}
void
TensorAttributeSaver::save_dense_tensor_store(BufferWriter& writer, const DenseTensorStore& dense_tensor_store) const
{
assert(get_header_version() == DENSE_TENSOR_ATTRIBUTE_VERSION);
auto raw_size = dense_tensor_store.getBufSize();
const uint32_t docid_limit(_refs.size());
for (uint32_t lid = 0; lid < docid_limit; ++lid) {
if (_refs[lid].valid()) {
auto raw = dense_tensor_store.getRawBuffer(_refs[lid]);
writer.write(&tensorIsPresent, sizeof(tensorIsPresent));
writer.write(static_cast<const char *>(raw), raw_size);
} else {
writer.write(&tensorIsNotPresent, sizeof(tensorIsNotPresent));
}
}
writer.flush();
}
}
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