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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "dense_dot_product_function.h"
#include <vespa/eval/eval/inline_operation.h>
#include <vespa/eval/eval/value.h>
namespace vespalib::eval {
using namespace tensor_function;
using namespace operation;
namespace {
template <typename LCT, typename RCT>
void my_dot_product_op(InterpretedFunction::State &state, uint64_t) {
auto lhs_cells = state.peek(1).cells().typify<LCT>();
auto rhs_cells = state.peek(0).cells().typify<RCT>();
double result = DotProduct<LCT,RCT>::apply(lhs_cells.cbegin(), rhs_cells.cbegin(), lhs_cells.size());
state.pop_pop_push(state.stash.create<DoubleValue>(result));
}
struct MyDotProductOp {
template <typename LCT, typename RCT>
static auto invoke() { return my_dot_product_op<LCT,RCT>; }
};
} // namespace <unnamed>
DenseDotProductFunction::DenseDotProductFunction(const TensorFunction &lhs_in,
const TensorFunction &rhs_in)
: tensor_function::Op2(ValueType::double_type(), lhs_in, rhs_in)
{
}
InterpretedFunction::Instruction
DenseDotProductFunction::compile_self(const ValueBuilderFactory &, Stash &) const
{
auto op = typify_invoke<2,TypifyCellType,MyDotProductOp>(lhs().result_type().cell_type(),
rhs().result_type().cell_type());
return InterpretedFunction::Instruction(op);
}
bool
DenseDotProductFunction::compatible_types(const ValueType &res, const ValueType &lhs, const ValueType &rhs)
{
return (res.is_double() && lhs.is_dense() && (rhs.dimensions() == lhs.dimensions()));
}
const TensorFunction &
DenseDotProductFunction::optimize(const TensorFunction &expr, Stash &stash)
{
auto reduce = as<Reduce>(expr);
if (reduce && (reduce->aggr() == Aggr::SUM)) {
auto join = as<Join>(reduce->child());
if (join && (join->function() == Mul::f)) {
const TensorFunction &lhs = join->lhs();
const TensorFunction &rhs = join->rhs();
if (compatible_types(expr.result_type(), lhs.result_type(), rhs.result_type())) {
return stash.create<DenseDotProductFunction>(lhs, rhs);
}
}
}
return expr;
}
} // namespace
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