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// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/vespalib/testkit/test_kit.h>
#include <vespa/eval/eval/tensor_function.h>
#include <vespa/eval/instruction/dense_replace_type_function.h>
#include <vespa/eval/instruction/dense_fast_rename_optimizer.h>
#include <vespa/eval/eval/test/tensor_model.hpp>
#include <vespa/eval/eval/test/eval_fixture.h>
#include <vespa/vespalib/util/stringfmt.h>
#include <vespa/vespalib/util/stash.h>
using namespace vespalib;
using namespace vespalib::eval;
using namespace vespalib::eval::test;
using namespace vespalib::eval::tensor_function;
const ValueBuilderFactory &prod_factory = FastValueBuilderFactory::get();
EvalFixture::ParamRepo make_params() {
return EvalFixture::ParamRepo()
.add("x1y5z1", spec({x(1),y(5),z(1)}, N()))
.add("x1y5z1f", spec(float_cells({x(1),y(5),z(1)}), N()))
.add("x1y1z1", spec({x(1),y(1),z(1)}, N()))
.add("x1y5z_m", spec({x(1),y(5),z({"a"})}, N()));
}
EvalFixture::ParamRepo param_repo = make_params();
void verify_optimized(const vespalib::string &expr) {
EvalFixture fixture(prod_factory, expr, param_repo, true);
EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
auto info = fixture.find_all<DenseReplaceTypeFunction>();
EXPECT_EQUAL(info.size(), 1u);
}
void verify_not_optimized(const vespalib::string &expr) {
EvalFixture fixture(prod_factory, expr, param_repo, true);
EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
auto info = fixture.find_all<DenseReplaceTypeFunction>();
EXPECT_TRUE(info.empty());
}
TEST("require that dimension removal can be optimized for appropriate aggregators") {
TEST_DO(verify_optimized("reduce(x1y5z1,avg,x)"));
TEST_DO(verify_not_optimized("reduce(x1y5z1,count,x)")); // NB
TEST_DO(verify_optimized("reduce(x1y5z1,prod,x)"));
TEST_DO(verify_optimized("reduce(x1y5z1,sum,x)"));
TEST_DO(verify_optimized("reduce(x1y5z1,max,x)"));
TEST_DO(verify_optimized("reduce(x1y5z1,min,x)"));
}
TEST("require that multi-dimension removal can be optimized") {
TEST_DO(verify_optimized("reduce(x1y5z1,sum,x,z)"));
}
TEST("require that chained dimension removal can be optimized (and compacted)") {
TEST_DO(verify_optimized("reduce(reduce(x1y5z1,sum,x),sum,z)"));
}
TEST("require that reducing non-trivial dimension is not optimized") {
TEST_DO(verify_not_optimized("reduce(x1y5z1,sum,y)"));
TEST_DO(verify_not_optimized("reduce(x1y5z1,sum,x,y)"));
TEST_DO(verify_not_optimized("reduce(x1y5z1,sum,y,z)"));
}
TEST("require that full reduce is not optimized") {
TEST_DO(verify_not_optimized("reduce(x1y1z1,sum)"));
TEST_DO(verify_not_optimized("reduce(x1y1z1,sum,x,y,z)"));
}
TEST("require that inappropriate tensor types cannot be optimized") {
TEST_DO(verify_not_optimized("reduce(x1y5z_m,sum,x)"));
TEST_DO(verify_not_optimized("reduce(x1y5z_m,sum,z)"));
}
TEST("require that optimization works for float cells") {
TEST_DO(verify_optimized("reduce(x1y5z1f,avg,x)"));
}
TEST_MAIN() { TEST_RUN_ALL(); }
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