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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_112_dot_product.h"
#include <vespa/eval/eval/fast_value.hpp>
#include <vespa/vespalib/util/typify.h>
#include <vespa/vespalib/util/require.h>
#include <vespa/eval/eval/visit_stuff.h>
#include <algorithm>
#include <optional>
namespace vespalib::eval {
using namespace tensor_function;
using namespace operation;
using namespace instruction;
namespace {
template <typename T, size_t N>
ConstArrayRef<const T *> as_ccar(std::array<T *, N> &array) {
return {array.data(), array.size()};
}
template <typename T>
ConstArrayRef<T> as_car(T &value) {
return {&value, 1};
}
constexpr std::array<size_t, 1> single_dim = { 0 };
template <typename CT>
double my_mixed_112_dot_product_fallback(const Value::Index &a_idx, const Value::Index &c_idx,
const CT *a_cells, const CT *b_cells, const CT *c_cells,
size_t dense_size) __attribute__((noinline));
template <typename CT>
double my_mixed_112_dot_product_fallback(const Value::Index &a_idx, const Value::Index &c_idx,
const CT *a_cells, const CT *b_cells, const CT *c_cells,
size_t dense_size)
{
double result = 0.0;
size_t a_space = 0;
size_t c_space = 0;
std::array<string_id, 1> c_addr;
std::array<string_id*, 1> c_addr_ref = {&c_addr[0]};
auto outer = a_idx.create_view({});
auto model = c_idx.create_view({&single_dim[0], 1});
outer->lookup({});
using dot_product = DotProduct<CT,CT>;
while (outer->next_result(as_car(c_addr_ref[0]), a_space)) {
model->lookup(as_ccar(c_addr_ref));
if (model->next_result({}, c_space)) {
result += dot_product::apply(b_cells, c_cells + (c_space * dense_size), dense_size) * a_cells[a_space];
}
}
return result;
}
template <typename CT>
double my_fast_mixed_112_dot_product(const FastAddrMap *a_map, const FastAddrMap *c_map,
const CT *a_cells, const CT *b_cells, const CT *c_cells,
size_t dense_size)
{
double result = 0.0;
const auto &a_labels = a_map->labels();
using dot_product = DotProduct<CT,CT>;
for (size_t a_space = 0; a_space < a_labels.size(); ++a_space) {
if (a_cells[a_space] != 0.0) { // handle pseudo-sparse input
auto c_space = c_map->lookup_singledim(a_labels[a_space]);
if (c_space != FastAddrMap::npos()) {
result += dot_product::apply(b_cells, c_cells + (c_space * dense_size), dense_size) * a_cells[a_space];
}
}
}
return result;
}
template <typename CT>
void my_mixed_112_dot_product_op(InterpretedFunction::State &state, uint64_t dense_size) {
const auto &a_idx = state.peek(2).index();
const auto &c_idx = state.peek(0).index();
const CT *a_cells = state.peek(2).cells().unsafe_typify<CT>().cbegin();
const CT *b_cells = state.peek(1).cells().unsafe_typify<CT>().cbegin();
const CT *c_cells = state.peek(0).cells().unsafe_typify<CT>().cbegin();
double result = __builtin_expect(are_fast(a_idx, c_idx), true)
? my_fast_mixed_112_dot_product<CT>(&as_fast(a_idx).map, &as_fast(c_idx).map,
a_cells, b_cells, c_cells, dense_size)
: my_mixed_112_dot_product_fallback<CT>(a_idx, c_idx, a_cells, b_cells, c_cells, dense_size);
state.pop_pop_pop_push(state.stash.create<DoubleValue>(result));
}
InterpretedFunction::op_function my_select(CellType cell_type) {
if (cell_type == CellType::DOUBLE) {
return my_mixed_112_dot_product_op<double>;
}
if (cell_type == CellType::FLOAT) {
return my_mixed_112_dot_product_op<float>;
}
abort();
}
// Try to collect input nodes and organize them into a 3-way dot
// product between one 1d sparse tensor, one 1d dense tensor and one
// 2d mixed tensor. Cell types must be all float or all double.
struct InputState {
std::optional<CellType> cell_type;
const TensorFunction *sparse = nullptr;
const TensorFunction *dense = nullptr;
const TensorFunction *mixed = nullptr;
bool failed = false;
void collect_cell_type(CellType ct) {
if (cell_type.has_value()) {
if (ct != cell_type.value()) {
failed = true;
}
} else {
cell_type = ct;
}
}
void try_save(const TensorFunction *&my_ptr, const TensorFunction &node) {
if (my_ptr == nullptr) {
my_ptr = &node;
} else {
failed = true;
}
}
void collect(const TensorFunction &node) {
const auto &type = node.result_type();
collect_cell_type(type.cell_type());
if (type.is_sparse()) {
try_save(sparse, node);
} else if (type.is_dense()) {
try_save(dense, node);
} else if (type.has_dimensions()) {
try_save(mixed, node);
} else {
failed = true;
}
}
bool verify() const {
// all parts must have been collected successfully
if (failed || !cell_type.has_value() || (sparse == nullptr) || (dense == nullptr) || (mixed == nullptr)) {
return false;
}
// common cell type must be appropriate
if ((cell_type.value() != CellType::FLOAT) && (cell_type.value() != CellType::DOUBLE)) {
return false;
}
// number of dimensions must match the expected 112 pattern
if ((sparse->result_type().dimensions().size() != 1) ||
(dense->result_type().dimensions().size() != 1) ||
(mixed->result_type().dimensions().size() != 2))
{
return false;
}
// the product of the sparse and dense tensors must fully overlap the mixed tensor
const ValueType::Dimension *mapped = &mixed->result_type().dimensions()[0];
const ValueType::Dimension *indexed = &mixed->result_type().dimensions()[1];
if (!mapped->is_mapped()) {
std::swap(mapped, indexed);
}
assert(mapped->is_mapped());
assert(indexed->is_indexed());
return ((*mapped == sparse->result_type().dimensions()[0]) &&
(*indexed == dense->result_type().dimensions()[0]));
}
};
// Try to find inputs that form a 112 mixed dot product.
struct FindInputs {
const TensorFunction *a = nullptr;
const TensorFunction *b = nullptr;
const TensorFunction *c = nullptr;
bool try_match(const TensorFunction &one, const TensorFunction &two) {
auto join = as<Join>(two);
if (join && (join->function() == Mul::f)) {
InputState state;
state.collect(one);
state.collect(join->lhs());
state.collect(join->rhs());
if (state.verify()) {
a = state.sparse;
b = state.dense;
c = state.mixed;
return true;
}
}
return false;
}
};
} // namespace <unnamed>
Mixed112DotProduct::Mixed112DotProduct(const TensorFunction &a_in,
const TensorFunction &b_in,
const TensorFunction &c_in)
: tensor_function::Node(DoubleValue::shared_type()),
_a(a_in),
_b(b_in),
_c(c_in)
{
}
InterpretedFunction::Instruction
Mixed112DotProduct::compile_self(const ValueBuilderFactory &, Stash &) const
{
REQUIRE_EQ(_a.get().result_type().cell_type(), _b.get().result_type().cell_type());
REQUIRE_EQ(_a.get().result_type().cell_type(), _c.get().result_type().cell_type());
REQUIRE_EQ(_b.get().result_type().dense_subspace_size(), _c.get().result_type().dense_subspace_size());
auto op = my_select(_a.get().result_type().cell_type());
return InterpretedFunction::Instruction(op, _c.get().result_type().dense_subspace_size());
}
void
Mixed112DotProduct::push_children(std::vector<Child::CREF> &children) const
{
children.emplace_back(_a);
children.emplace_back(_b);
children.emplace_back(_c);
}
void
Mixed112DotProduct::visit_children(vespalib::ObjectVisitor &visitor) const
{
::visit(visitor, "a", _a.get());
::visit(visitor, "b", _b.get());
::visit(visitor, "c", _c.get());
}
const TensorFunction &
Mixed112DotProduct::optimize(const TensorFunction &expr, Stash &stash)
{
auto reduce = as<Reduce>(expr);
if (reduce && (reduce->aggr() == Aggr::SUM) && expr.result_type().is_double()) {
auto join = as<Join>(reduce->child());
if (join && (join->function() == Mul::f)) {
FindInputs inputs;
if (inputs.try_match(join->lhs(), join->rhs()) ||
inputs.try_match(join->rhs(), join->lhs()))
{
return stash.create<Mixed112DotProduct>(*inputs.a, *inputs.b, *inputs.c);
}
}
}
return expr;
}
} // namespace
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