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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "dense_tensor_store.h"
#include <vespa/eval/eval/value.h>
#include <vespa/vespalib/datastore/compacting_buffers.h>
#include <vespa/vespalib/datastore/compaction_context.h>
#include <vespa/vespalib/datastore/compaction_strategy.h>
#include <vespa/vespalib/datastore/datastore.hpp>
#include <vespa/vespalib/util/memory_allocator.h>
using vespalib::datastore::CompactionContext;
using vespalib::datastore::CompactionSpec;
using vespalib::datastore::CompactionStrategy;
using vespalib::datastore::EntryRef;
using vespalib::datastore::Handle;
using vespalib::datastore::ICompactionContext;
using vespalib::eval::CellType;
using vespalib::eval::CellTypeUtils;
using vespalib::eval::Value;
using vespalib::eval::ValueType;
namespace search::tensor {
namespace {
constexpr size_t MIN_BUFFER_ARRAYS = 1024;
constexpr size_t DENSE_TENSOR_ALIGNMENT = 32;
constexpr size_t DENSE_TENSOR_ALIGNMENT_SMALL = 16;
constexpr size_t DENSE_TENSOR_ALIGNMENT_MIN = 8;
size_t my_align(size_t size, size_t alignment) {
size += alignment - 1;
return (size - (size % alignment));
}
}
DenseTensorStore::TensorSizeCalc::TensorSizeCalc(const ValueType &type)
: _numCells(1u),
_cell_type(type.cell_type()),
_aligned_size(0u)
{
for (const auto &dim: type.dimensions()) {
_numCells *= dim.size;
}
auto buf_size = bufSize();
size_t alignment = DENSE_TENSOR_ALIGNMENT;
if (buf_size <= DENSE_TENSOR_ALIGNMENT_MIN) {
alignment = DENSE_TENSOR_ALIGNMENT_MIN;
} else if (buf_size <= DENSE_TENSOR_ALIGNMENT_SMALL) {
alignment = DENSE_TENSOR_ALIGNMENT_SMALL;
}
_aligned_size = my_align(buf_size, alignment);
}
DenseTensorStore::BufferType::BufferType(const TensorSizeCalc &tensorSizeCalc, std::shared_ptr<vespalib::alloc::MemoryAllocator> allocator)
: vespalib::datastore::BufferType<char>(tensorSizeCalc.alignedSize(), MIN_BUFFER_ARRAYS, RefType::offsetSize()),
_allocator(std::move(allocator))
{}
DenseTensorStore::BufferType::~BufferType() = default;
void
DenseTensorStore::BufferType::cleanHold(void *buffer, size_t offset,
ElemCount numElems, CleanContext)
{
memset(static_cast<char *>(buffer) + offset, 0, numElems);
}
const vespalib::alloc::MemoryAllocator*
DenseTensorStore::BufferType::get_memory_allocator() const
{
return _allocator.get();
}
DenseTensorStore::DenseTensorStore(const ValueType &type, std::shared_ptr<vespalib::alloc::MemoryAllocator> allocator)
: TensorStore(_concreteStore),
_concreteStore(),
_tensorSizeCalc(type),
_bufferType(_tensorSizeCalc, std::move(allocator)),
_type(type),
_emptySpace()
{
_emptySpace.resize(getBufSize(), 0);
_store.addType(&_bufferType);
_store.init_primary_buffers();
_store.enableFreeLists();
}
DenseTensorStore::~DenseTensorStore()
{
_store.dropBuffers();
}
namespace {
void clearPadAreaAfterBuffer(char *buffer, size_t bufSize, size_t alignedBufSize) {
size_t padSize = alignedBufSize - bufSize;
memset(buffer + bufSize, 0, padSize);
}
}
Handle<char>
DenseTensorStore::allocRawBuffer()
{
size_t bufSize = getBufSize();
size_t alignedBufSize = _tensorSizeCalc.alignedSize();
auto result = _concreteStore.freeListRawAllocator<char>(0u).alloc(alignedBufSize);
clearPadAreaAfterBuffer(result.data, bufSize, alignedBufSize);
return result;
}
void
DenseTensorStore::holdTensor(EntryRef ref)
{
if (!ref.valid()) {
return;
}
_concreteStore.holdElem(ref, _tensorSizeCalc.alignedSize());
}
TensorStore::EntryRef
DenseTensorStore::move(EntryRef ref)
{
if (!ref.valid()) {
return RefType();
}
auto oldraw = getRawBuffer(ref);
auto newraw = allocRawBuffer();
memcpy(newraw.data, static_cast<const char *>(oldraw), getBufSize());
_concreteStore.holdElem(ref, _tensorSizeCalc.alignedSize());
return newraw.ref;
}
vespalib::MemoryUsage
DenseTensorStore::update_stat(const CompactionStrategy& compaction_strategy)
{
auto memory_usage = _store.getMemoryUsage();
_compaction_spec = CompactionSpec(compaction_strategy.should_compact_memory(memory_usage), false);
return memory_usage;
}
std::unique_ptr<ICompactionContext>
DenseTensorStore::start_compact(const CompactionStrategy& compaction_strategy)
{
auto compacting_buffers = _store.start_compact_worst_buffers(_compaction_spec, compaction_strategy);
return std::make_unique<CompactionContext>(*this, std::move(compacting_buffers));
}
EntryRef
DenseTensorStore::store_tensor(const Value& tensor)
{
assert(tensor.type() == _type);
auto cells = tensor.cells();
assert(cells.size == getNumCells());
assert(cells.type == _type.cell_type());
auto raw = allocRawBuffer();
memcpy(raw.data, cells.data, getBufSize());
return raw.ref;
}
EntryRef
DenseTensorStore::store_encoded_tensor(vespalib::nbostream& encoded)
{
(void) encoded;
abort();
}
std::unique_ptr<Value>
DenseTensorStore::get_tensor(EntryRef ref) const
{
if (!ref.valid()) {
return {};
}
vespalib::eval::TypedCells cells_ref(getRawBuffer(ref), _type.cell_type(), getNumCells());
return std::make_unique<vespalib::eval::DenseValueView>(_type, cells_ref);
}
bool
DenseTensorStore::encode_stored_tensor(EntryRef ref, vespalib::nbostream& target) const
{
(void) ref;
(void) target;
abort();
}
}
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