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// Copyright Yahoo. 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_tensor_create_function.h>
#include <vespa/eval/eval/test/gen_spec.h>
#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("a", GenSpec(1.0))
.add("b", GenSpec(2.0))
.add("c", GenSpec(3.0));
}
EvalFixture::ParamRepo param_repo = make_params();
void verify(const vespalib::string &expr, size_t expect_optimized_cnt, size_t expect_not_optimized_cnt) {
EvalFixture fixture(prod_factory, expr, param_repo, true);
EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
auto info = fixture.find_all<DenseTensorCreateFunction>();
EXPECT_EQUAL(info.size(), expect_optimized_cnt);
for (size_t i = 0; i < info.size(); ++i) {
EXPECT_TRUE(info[i]->result_is_mutable());
}
EXPECT_EQUAL(fixture.find_all<Create>().size(), expect_not_optimized_cnt);
}
//-----------------------------------------------------------------------------
TEST("require that tensor create can be optimized") {
TEST_DO(verify("tensor(x[3]):{{x:0}:1,{x:1}:2,{x:2}:3}", 0, 0)); // NB: const value
TEST_DO(verify("tensor(x[3]):{{x:0}:a,{x:1}:b,{x:2}:c}", 1, 0));
TEST_DO(verify("tensor<float>(x[3]):{{x:0}:a,{x:1}:b,{x:2}:c}", 1, 0));
TEST_DO(verify("tensor(x[3]):{{x:0}:a+b,{x:1}:b-c,{x:2}:c*a}", 1, 0));
}
TEST("require that tensor create can be optimized with missing cells (padded with 0.0)") {
TEST_DO(verify("tensor(x[3],y[5]):{{x:0,y:1}:a,{x:1,y:3}:b,{x:2,y:4}:c}", 1, 0));
}
TEST("require that tensor create in not optimized for sparse tensor") {
TEST_DO(verify("tensor(x{}):{{x:0}:a,{x:1}:b,{x:2}:c}", 0, 1));
}
TEST("require that tensor create in not optimized for mixed tensor") {
TEST_DO(verify("tensor(x{},y[3]):{{x:a,y:0}:a,{x:a,y:1}:b,{x:a,y:2}:c}", 0, 1));
}
TEST_MAIN() { TEST_RUN_ALL(); }
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