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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "mixed_simple_join_function.h"
#include <vespa/vespalib/objects/objectvisitor.h>
#include <vespa/eval/eval/value.h>
#include <vespa/eval/eval/operation.h>
#include <vespa/eval/eval/inline_operation.h>
#include <vespa/vespalib/util/typify.h>
#include <optional>
#include <algorithm>
namespace vespalib::eval {
using vespalib::ArrayRef;
using namespace operation;
using namespace tensor_function;
using Primary = MixedSimpleJoinFunction::Primary;
using Overlap = MixedSimpleJoinFunction::Overlap;
using op_function = InterpretedFunction::op_function;
using Instruction = InterpretedFunction::Instruction;
using State = InterpretedFunction::State;
namespace {
struct TypifyOverlap {
template <Overlap VALUE> using Result = TypifyResultValue<Overlap, VALUE>;
template <typename F> static decltype(auto) resolve(Overlap value, F &&f) {
switch (value) {
case Overlap::INNER: return f(Result<Overlap::INNER>());
case Overlap::OUTER: return f(Result<Overlap::OUTER>());
case Overlap::FULL: return f(Result<Overlap::FULL>());
}
abort();
}
};
struct JoinParams {
const ValueType &result_type;
size_t factor;
size_t subspace_size;
join_fun_t function;
JoinParams(const ValueType &result_type_in, size_t factor_in, join_fun_t function_in)
: result_type(result_type_in), factor(factor_in), subspace_size(result_type.dense_subspace_size()), function(function_in) {}
};
template <typename OCT, bool pri_mut, typename PCT>
ArrayRef<OCT> make_dst_cells(ConstArrayRef<PCT> pri_cells, Stash &stash) {
if constexpr (pri_mut && std::is_same<PCT,OCT>::value) {
return unconstify(pri_cells);
} else {
return stash.create_uninitialized_array<OCT>(pri_cells.size());
}
}
template <typename LCT, typename RCT, typename OCT, typename Fun, bool swap, Overlap overlap, bool pri_mut>
void my_simple_join_op(State &state, uint64_t param) {
using PCT = typename std::conditional<swap,RCT,LCT>::type;
using SCT = typename std::conditional<swap,LCT,RCT>::type;
using OP = typename std::conditional<swap,SwapArgs2<Fun>,Fun>::type;
const JoinParams ¶ms = unwrap_param<JoinParams>(param);
OP my_op(params.function);
auto pri_cells = state.peek(swap ? 0 : 1).cells().typify<PCT>();
auto sec_cells = state.peek(swap ? 1 : 0).cells().typify<SCT>();
auto dst_cells = make_dst_cells<OCT, pri_mut>(pri_cells, state.stash);
const auto &index = state.peek(swap ? 0 : 1).index();
size_t subspace_size = params.subspace_size;
size_t offset = 0;
while (offset < pri_cells.size()) {
if constexpr (overlap == Overlap::FULL) {
apply_op2_vec_vec(&dst_cells[offset], &pri_cells[offset], sec_cells.begin(), subspace_size, my_op);
offset += subspace_size;
} else if constexpr (overlap == Overlap::OUTER) {
size_t factor = params.factor;
for (SCT cell: sec_cells) {
apply_op2_vec_num(&dst_cells[offset], &pri_cells[offset], cell, factor, my_op);
offset += factor;
}
} else {
static_assert(overlap == Overlap::INNER);
size_t factor = params.factor;
for (size_t i = 0; i < factor; ++i) {
apply_op2_vec_vec(&dst_cells[offset], &pri_cells[offset], sec_cells.begin(), sec_cells.size(), my_op);
offset += sec_cells.size();
}
}
}
assert(offset == pri_cells.size());
state.pop_pop_push(state.stash.create<ValueView>(params.result_type, index, TypedCells(dst_cells)));
}
//-----------------------------------------------------------------------------
struct SelectMixedSimpleJoin {
template<typename LCM, typename RCM, typename R3, typename R4, typename R5, typename R6>
static auto invoke() {
constexpr CellMeta ocm = CellMeta::join(LCM::value, RCM::value);
using LCT = CellValueType<LCM::value.cell_type>;
using RCT = CellValueType<RCM::value.cell_type>;
using OCT = CellValueType<ocm.cell_type>;
return my_simple_join_op<LCT, RCT, OCT, R3, R4::value, R5::value, R6::value>;
}
};
using MyTypify = TypifyValue<TypifyCellMeta,TypifyOp2,TypifyBool,TypifyOverlap>;
//-----------------------------------------------------------------------------
bool can_use_as_output(const TensorFunction &fun, CellType result_cell_type) {
return (fun.result_is_mutable() && (fun.result_type().cell_type() == result_cell_type));
}
Primary select_primary(const TensorFunction &lhs, const TensorFunction &rhs, CellType result_cell_type) {
if (!lhs.result_type().is_dense()) {
return Primary::LHS;
} else if (!rhs.result_type().is_dense()) {
return Primary::RHS;
}
size_t lhs_size = lhs.result_type().dense_subspace_size();
size_t rhs_size = rhs.result_type().dense_subspace_size();
if (lhs_size > rhs_size) {
return Primary::LHS;
} else if (rhs_size > lhs_size) {
return Primary::RHS;
} else {
bool can_write_lhs = can_use_as_output(lhs, result_cell_type);
bool can_write_rhs = can_use_as_output(rhs, result_cell_type);
if (can_write_lhs && !can_write_rhs) {
return Primary::LHS;
} else {
// prefer using rhs as output due to write recency
return Primary::RHS;
}
}
}
std::optional<Overlap> detect_overlap(const TensorFunction &primary, const TensorFunction &secondary) {
std::vector<ValueType::Dimension> a = primary.result_type().nontrivial_indexed_dimensions();
std::vector<ValueType::Dimension> b = secondary.result_type().nontrivial_indexed_dimensions();
assert(secondary.result_type().is_dense());
if (b.size() > a.size()) {
return std::nullopt;
} else if (b == a) {
return Overlap::FULL;
} else if (std::equal(b.begin(), b.end(), a.begin())) {
// prefer OUTER to INNER (for empty b) due to loop nesting
return Overlap::OUTER;
} else if (std::equal(b.rbegin(), b.rend(), a.rbegin())) {
return Overlap::INNER;
} else {
return std::nullopt;
}
}
std::optional<Overlap> detect_overlap(const TensorFunction &lhs, const TensorFunction &rhs, Primary primary) {
return (primary == Primary::LHS) ? detect_overlap(lhs, rhs) : detect_overlap(rhs, lhs);
}
} // namespace vespalib::eval::<unnamed>
//-----------------------------------------------------------------------------
MixedSimpleJoinFunction::MixedSimpleJoinFunction(const ValueType &result_type,
const TensorFunction &lhs,
const TensorFunction &rhs,
join_fun_t function_in,
Primary primary_in,
Overlap overlap_in)
: Join(result_type, lhs, rhs, function_in),
_primary(primary_in),
_overlap(overlap_in)
{
}
MixedSimpleJoinFunction::~MixedSimpleJoinFunction() = default;
const TensorFunction &
MixedSimpleJoinFunction::primary_child() const
{
return (_primary == Primary::LHS) ? lhs() : rhs();
}
bool
MixedSimpleJoinFunction::primary_is_mutable() const
{
return primary_child().result_is_mutable();
}
bool
MixedSimpleJoinFunction::inplace() const
{
return primary_is_mutable() &&
(result_type().cell_type() == primary_child().result_type().cell_type());
}
size_t
MixedSimpleJoinFunction::factor() const
{
const TensorFunction &p = (_primary == Primary::LHS) ? lhs() : rhs();
const TensorFunction &s = (_primary == Primary::LHS) ? rhs() : lhs();
size_t a = p.result_type().dense_subspace_size();
size_t b = s.result_type().dense_subspace_size();
assert((a % b) == 0);
return (a / b);
}
Instruction
MixedSimpleJoinFunction::compile_self(const CTFContext &ctx) const
{
const JoinParams ¶ms = ctx.stash.create<JoinParams>(result_type(), factor(), function());
auto op = typify_invoke<6,MyTypify,SelectMixedSimpleJoin>(lhs().result_type().cell_meta().not_scalar(),
rhs().result_type().cell_meta().not_scalar(),
function(), (_primary == Primary::RHS),
_overlap, primary_is_mutable());
return Instruction(op, wrap_param<JoinParams>(params));
}
const TensorFunction &
MixedSimpleJoinFunction::optimize(const TensorFunction &expr, Stash &stash)
{
if (auto join = as<Join>(expr)) {
const TensorFunction &lhs = join->lhs();
const TensorFunction &rhs = join->rhs();
if (lhs.result_type().is_dense() || rhs.result_type().is_dense()) {
Primary primary = select_primary(lhs, rhs, join->result_type().cell_type());
std::optional<Overlap> overlap = detect_overlap(lhs, rhs, primary);
if (overlap.has_value()) {
const TensorFunction &ptf = (primary == Primary::LHS) ? lhs : rhs;
assert(ptf.result_type().dense_subspace_size() == join->result_type().dense_subspace_size());
return stash.create<MixedSimpleJoinFunction>(join->result_type(), lhs, rhs, join->function(),
primary, overlap.value());
}
}
}
return expr;
}
} // namespace vespalib::eval
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