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// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/eval/eval/simple_value.h>
#include <vespa/eval/eval/fast_value.h>
#include <vespa/eval/eval/value_codec.h>
#include <vespa/eval/instruction/generic_join.h>
#include <vespa/eval/eval/interpreted_function.h>
#include <vespa/eval/eval/test/reference_operations.h>
#include <vespa/eval/eval/test/gen_spec.h>
#include <vespa/vespalib/util/stringfmt.h>
#include <vespa/vespalib/gtest/gtest.h>
using namespace vespalib;
using namespace vespalib::eval;
using namespace vespalib::eval::instruction;
using namespace vespalib::eval::test;
using vespalib::make_string_short::fmt;
GenSpec::seq_t N_16ths = [] (size_t i) noexcept { return (i + 1.0) / 16.0; };
GenSpec G() { return GenSpec().cells_float().seq(N_16ths); }
std::vector<GenSpec> join_layouts = {
G(), G(),
G().idx("x", 5), G().idx("x", 5),
G().idx("x", 5), G().idx("y", 5),
G().idx("x", 5), G().idx("x", 5).idx("y", 5),
G().idx("y", 3), G().idx("x", 2).idx("z", 3),
G().idx("x", 3).idx("y", 5), G().idx("y", 5).idx("z", 7),
G().map("x", {"a","b","c"}), G().map("x", {"a","b","c"}),
G().map("x", {"a","b","c"}), G().map("x", {"a","b"}),
G().map("x", {"a","b","c"}), G().map("y", {"foo","bar","baz"}),
G().map("x", {"a","b","c"}), G().map("x", {"a","b","c"}).map("y", {"foo","bar","baz"}),
G().map("x", {"a","b"}).map("y", {"foo","bar","baz"}), G().map("x", {"a","b","c"}).map("y", {"foo","bar"}),
G().map("x", {"a","b"}).map("y", {"foo","bar","baz"}), G().map("y", {"foo","bar"}).map("z", {"i","j","k","l"}),
G().idx("x", 3).map("y", {"foo", "bar"}), G().map("y", {"foo", "bar"}).idx("z", 7),
G().map("x", {"a","b","c"}).idx("y", 5), G().idx("y", 5).map("z", {"i","j","k","l"})
};
bool join_address(const TensorSpec::Address &a, const TensorSpec::Address &b, TensorSpec::Address &addr) {
for (const auto &dim_a: a) {
auto pos_b = b.find(dim_a.first);
if ((pos_b != b.end()) && !(pos_b->second == dim_a.second)) {
return false;
}
addr.insert_or_assign(dim_a.first, dim_a.second);
}
return true;
}
TensorSpec perform_generic_join(const TensorSpec &a, const TensorSpec &b,
join_fun_t function, const ValueBuilderFactory &factory)
{
Stash stash;
auto lhs = value_from_spec(a, factory);
auto rhs = value_from_spec(b, factory);
auto my_op = GenericJoin::make_instruction(lhs->type(), rhs->type(), function, factory, stash);
InterpretedFunction::EvalSingle single(factory, my_op);
return spec_from_value(single.eval(std::vector<Value::CREF>({*lhs,*rhs})));
}
TEST(GenericJoinTest, dense_join_plan_can_be_created) {
auto lhs = ValueType::from_spec("tensor(a{},b[6],c[5],e[3],f[2],g{})");
auto rhs = ValueType::from_spec("tensor(a{},b[6],c[5],d[4],h{})");
auto plan = DenseJoinPlan(lhs, rhs);
std::vector<size_t> expect_loop = {30,4,6};
std::vector<size_t> expect_lhs_stride = {6,0,1};
std::vector<size_t> expect_rhs_stride = {4,1,0};
EXPECT_EQ(plan.lhs_size, 180);
EXPECT_EQ(plan.rhs_size, 120);
EXPECT_EQ(plan.out_size, 720);
EXPECT_EQ(plan.loop_cnt, expect_loop);
EXPECT_EQ(plan.lhs_stride, expect_lhs_stride);
EXPECT_EQ(plan.rhs_stride, expect_rhs_stride);
}
TEST(GenericJoinTest, sparse_join_plan_can_be_created) {
auto lhs = ValueType::from_spec("tensor(a{},b[6],c[5],e[3],f[2],g{})");
auto rhs = ValueType::from_spec("tensor(b[6],c[5],d[4],g{},h{})");
auto plan = SparseJoinPlan(lhs, rhs);
using SRC = SparseJoinPlan::Source;
std::vector<SRC> expect_sources = {SRC::LHS,SRC::BOTH,SRC::RHS};
std::vector<size_t> expect_lhs_overlap = {1};
std::vector<size_t> expect_rhs_overlap = {0};
EXPECT_EQ(plan.sources, expect_sources);
EXPECT_EQ(plan.lhs_overlap, expect_lhs_overlap);
EXPECT_EQ(plan.rhs_overlap, expect_rhs_overlap);
}
TEST(GenericJoinTest, dense_join_plan_can_be_executed) {
auto plan = DenseJoinPlan(ValueType::from_spec("tensor(a[2])"),
ValueType::from_spec("tensor(b[3])"));
std::vector<int> a({1, 2});
std::vector<int> b({3, 4, 5});
std::vector<int> c(6, 0);
std::vector<int> expect = {3,4,5,6,8,10};
ASSERT_EQ(plan.out_size, 6);
int *dst = &c[0];
auto cell_join = [&](size_t a_idx, size_t b_idx) { *dst++ = (a[a_idx] * b[b_idx]); };
plan.execute(0, 0, cell_join);
EXPECT_EQ(c, expect);
}
TEST(GenericJoinTest, generic_join_works_for_simple_and_fast_values) {
ASSERT_TRUE((join_layouts.size() % 2) == 0);
for (size_t i = 0; i < join_layouts.size(); i += 2) {
const auto &l = join_layouts[i];
const auto &r = join_layouts[i+1];
for (TensorSpec lhs : { l.gen(), l.cpy().cells_double().gen() }) {
for (TensorSpec rhs : { r.gen(), r.cpy().cells_double().gen() }) {
for (auto fun: {operation::Add::f, operation::Sub::f, operation::Mul::f, operation::Div::f}) {
SCOPED_TRACE(fmt("\n===\nLHS: %s\nRHS: %s\n===\n", lhs.to_string().c_str(), rhs.to_string().c_str()));
auto expect = ReferenceOperations::join(lhs, rhs, fun);
auto simple = perform_generic_join(lhs, rhs, fun, SimpleValueBuilderFactory::get());
auto fast = perform_generic_join(lhs, rhs, fun, FastValueBuilderFactory::get());
EXPECT_EQ(simple, expect);
EXPECT_EQ(fast, expect);
}
}
}
}
}
GTEST_MAIN_RUN_ALL_TESTS()
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