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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "distance_calculator.h"
#include "distance_function_factory.h"
#include "nearest_neighbor_index.h"
#include <vespa/eval/eval/fast_value.h>
#include <vespa/searchcommon/attribute/iattributevector.h>
#include <vespa/vespalib/util/exceptions.h>
#include <vespa/vespalib/util/stringfmt.h>
using vespalib::IllegalArgumentException;
using vespalib::eval::CellType;
using vespalib::eval::FastValueBuilderFactory;
using vespalib::eval::TypedCells;
using vespalib::eval::Value;
using vespalib::eval::ValueType;
using vespalib::make_string;
namespace {
template<typename LCT, typename RCT>
std::unique_ptr<Value>
convert_cells(const ValueType& new_type, const Value& old_value)
{
auto old_cells = old_value.cells().typify<LCT>();
auto builder = FastValueBuilderFactory::get().create_value_builder<RCT>(new_type);
auto new_cells = builder->add_subspace();
assert(old_cells.size() == new_cells.size());
auto p = new_cells.begin();
for (LCT value : old_cells) {
RCT conv(value);
*p++ = conv;
}
return builder->build(std::move(builder));
}
struct ConvertCellsSelector
{
template <typename LCT, typename RCT>
static auto invoke(const ValueType& new_type, const Value& old_value) {
return convert_cells<LCT, RCT>(new_type, old_value);
}
auto operator() (CellType from, CellType to, const Value& old_value) const {
using MyTypify = vespalib::eval::TypifyCellType;
ValueType new_type = old_value.type().cell_cast(to);
return vespalib::typify_invoke<2,MyTypify,ConvertCellsSelector>(from, to, new_type, old_value);
}
};
}
namespace search::tensor {
DistanceCalculator::DistanceCalculator(const tensor::ITensorAttribute& attr_tensor,
const vespalib::eval::Value& query_tensor_in)
: _attr_tensor(attr_tensor),
_query_tensor_uptr(),
_query_tensor(&query_tensor_in),
_dist_fun()
{
auto * nns_index = _attr_tensor.nearest_neighbor_index();
auto & dff = nns_index ? nns_index->distance_function_factory() : attr_tensor.distance_function_factory();
auto query_ct = _query_tensor->cells().type;
CellType required_ct = dff.expected_cell_type;
if (query_ct != required_ct) {
ConvertCellsSelector converter;
_query_tensor_uptr = converter(query_ct, required_ct, *_query_tensor);
_query_tensor = _query_tensor_uptr.get();
}
_dist_fun = dff.for_query_vector(_query_tensor->cells());
assert(_dist_fun);
}
DistanceCalculator::DistanceCalculator(const tensor::ITensorAttribute& attr_tensor,
BoundDistanceFunction::UP function_in)
: _attr_tensor(attr_tensor),
_query_tensor_uptr(),
_query_tensor(nullptr),
_dist_fun(std::move(function_in))
{
}
DistanceCalculator::~DistanceCalculator() = default;
namespace {
bool
supported_tensor_type(const vespalib::eval::ValueType& type)
{
if (type.is_dense() && type.dimensions().size() == 1) {
return true;
}
if (type.is_mixed() && type.dimensions().size() == 2) {
return true;
}
return false;
}
bool
is_compatible(const vespalib::eval::ValueType& lhs,
const vespalib::eval::ValueType& rhs)
{
return (lhs.indexed_dimensions() == rhs.indexed_dimensions());
}
}
std::unique_ptr<DistanceCalculator>
DistanceCalculator::make_with_validation(const search::attribute::IAttributeVector& attr,
const vespalib::eval::Value& query_tensor_in)
{
const ITensorAttribute* attr_tensor = attr.asTensorAttribute();
if (attr_tensor == nullptr) {
throw IllegalArgumentException("Attribute is not a tensor");
}
const auto& at_type = attr_tensor->getTensorType();
if (!supported_tensor_type(at_type)) {
throw IllegalArgumentException(make_string("Attribute tensor type (%s) is not supported",
at_type.to_spec().c_str()));
}
const auto& qt_type = query_tensor_in.type();
if (!qt_type.is_dense()) {
throw IllegalArgumentException(make_string("Query tensor type (%s) is not a dense tensor",
qt_type.to_spec().c_str()));
}
if (!is_compatible(at_type, qt_type)) {
throw IllegalArgumentException(make_string("Attribute tensor type (%s) and query tensor type (%s) are not compatible",
at_type.to_spec().c_str(), qt_type.to_spec().c_str()));
}
return std::make_unique<DistanceCalculator>(*attr_tensor, query_tensor_in);
}
}
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