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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/vespalib/testkit/test_kit.h>
#include <vespa/eval/eval/tensor_function.h>
#include <vespa/eval/eval/simple_tensor.h>
#include <vespa/eval/eval/simple_tensor_engine.h>
#include <vespa/eval/tensor/default_tensor_engine.h>
#include <vespa/eval/tensor/dense/dense_replace_type_function.h>
#include <vespa/eval/tensor/dense/dense_cell_range_function.h>
#include <vespa/eval/tensor/dense/dense_lambda_peek_function.h>
#include <vespa/eval/tensor/dense/dense_fast_rename_optimizer.h>
#include <vespa/eval/tensor/dense/dense_tensor.h>
#include <vespa/eval/eval/test/tensor_model.hpp>
#include <vespa/eval/eval/test/eval_fixture.h>
#include <vespa/eval/eval/tensor_nodes.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::tensor;
using namespace vespalib::eval::tensor_function;
const TensorEngine &prod_engine = DefaultTensorEngine::ref();
EvalFixture::ParamRepo make_params() {
return EvalFixture::ParamRepo()
.add("a", spec(1))
.add("x3", spec({x(3)}, N()))
.add("x3f", spec(float_cells({x(3)}), N()))
.add("x3m", spec({x({"0", "1", "2"})}, N()))
.add("x3y5", spec({x(3), y(5)}, N()))
.add("x3y5f", spec(float_cells({x(3), y(5)}), N()))
.add("x15", spec({x(15)}, N()))
.add("x15f", spec(float_cells({x(15)}), N()));
}
EvalFixture::ParamRepo param_repo = make_params();
template <typename T, typename F>
void verify_impl(const vespalib::string &expr, const vespalib::string &expect, F &&inspect) {
EvalFixture fixture(prod_engine, expr, param_repo, true);
EvalFixture slow_fixture(prod_engine, expr, param_repo, false);
EXPECT_EQUAL(fixture.result(), slow_fixture.result());
EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expect, param_repo));
auto info = fixture.find_all<T>();
if (EXPECT_EQUAL(info.size(), 1u)) {
inspect(info[0]);
}
}
template <typename T>
void verify_impl(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<T>(expr, expect, [](const T*){});
}
void verify_generic(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<Lambda>(expr, expect);
}
void verify_reshape(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<DenseReplaceTypeFunction>(expr, expect);
}
void verify_range(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<DenseCellRangeFunction>(expr, expect);
}
void verify_compiled(const vespalib::string &expr, const vespalib::string &expect,
const vespalib::string &expect_idx_fun)
{
verify_impl<DenseLambdaPeekFunction>(expr, expect,
[&](const DenseLambdaPeekFunction *info)
{
EXPECT_EQUAL(info->idx_fun_dump(), expect_idx_fun);
});
}
void verify_const(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<ConstValue>(expr, expect);
}
//-----------------------------------------------------------------------------
TEST("require that simple constant tensor lambda works") {
TEST_DO(verify_const("tensor(x[3])(x+1)", "tensor(x[3]):[1,2,3]"));
}
TEST("require that simple dynamic tensor lambda works") {
TEST_DO(verify_generic("tensor(x[3])(x+a)", "tensor(x[3]):[1,2,3]"));
}
TEST("require that tensor lambda can be used for tensor slicing") {
TEST_DO(verify_generic("tensor(x[2])(x3{x:(x+a)})", "tensor(x[2]):[2,3]"));
TEST_DO(verify_generic("tensor(x[2])(a+x3{x:(x)})", "tensor(x[2]):[2,3]"));
}
TEST("require that tensor lambda can be used for cell type casting") {
TEST_DO(verify_compiled("tensor(x[3])(x3f{x:(x)})", "tensor(x[3]):[1,2,3]", "f(x)(x)"));
TEST_DO(verify_compiled("tensor<float>(x[3])(x3{x:(x)})", "tensor<float>(x[3]):[1,2,3]", "f(x)(x)"));
}
TEST("require that tensor lambda can be used to convert from sparse to dense tensors") {
TEST_DO(verify_generic("tensor(x[3])(x3m{x:(x)})", "tensor(x[3]):[1,2,3]"));
TEST_DO(verify_generic("tensor(x[2])(x3m{x:(x)})", "tensor(x[2]):[1,2]"));
}
TEST("require that constant nested tensor lambda using tensor peek works") {
TEST_DO(verify_const("tensor(x[2])(tensor(y[2])((x+y)+1){y:(x)})", "tensor(x[2]):[1,3]"));
}
TEST("require that dynamic nested tensor lambda using tensor peek works") {
TEST_DO(verify_generic("tensor(x[2])(tensor(y[2])((x+y)+a){y:(x)})", "tensor(x[2]):[1,3]"));
}
TEST("require that tensor reshape is optimized") {
TEST_DO(verify_reshape("tensor(x[15])(x3y5{x:(x/5),y:(x%5)})", "x15"));
TEST_DO(verify_reshape("tensor(x[3],y[5])(x15{x:(x*5+y)})", "x3y5"));
TEST_DO(verify_reshape("tensor<float>(x[15])(x3y5f{x:(x/5),y:(x%5)})", "x15f"));
}
TEST("require that tensor reshape with non-matching cell type requires cell copy") {
TEST_DO(verify_compiled("tensor(x[15])(x3y5f{x:(x/5),y:(x%5)})", "x15", "f(x)((floor((x/5))*5)+(x%5))"));
TEST_DO(verify_compiled("tensor<float>(x[15])(x3y5{x:(x/5),y:(x%5)})", "x15f", "f(x)((floor((x/5))*5)+(x%5))"));
TEST_DO(verify_compiled("tensor(x[3],y[5])(x15f{x:(x*5+y)})", "x3y5", "f(x,y)((x*5)+y)"));
TEST_DO(verify_compiled("tensor<float>(x[3],y[5])(x15{x:(x*5+y)})", "x3y5f", "f(x,y)((x*5)+y)"));
}
TEST("require that tensor cell subrange view is optimized") {
TEST_DO(verify_range("tensor(y[5])(x3y5{x:1,y:(y)})", "x3y5{x:1}"));
TEST_DO(verify_range("tensor(x[3])(x15{x:(x+5)})", "tensor(x[3]):[6,7,8]"));
TEST_DO(verify_range("tensor<float>(y[5])(x3y5f{x:1,y:(y)})", "x3y5f{x:1}"));
TEST_DO(verify_range("tensor<float>(x[3])(x15f{x:(x+5)})", "tensor<float>(x[3]):[6,7,8]"));
}
TEST("require that tensor cell subrange with non-matching cell type requires cell copy") {
TEST_DO(verify_compiled("tensor(x[3])(x15f{x:(x+5)})", "tensor(x[3]):[6,7,8]", "f(x)(x+5)"));
TEST_DO(verify_compiled("tensor<float>(x[3])(x15{x:(x+5)})", "tensor<float>(x[3]):[6,7,8]", "f(x)(x+5)"));
}
TEST("require that non-continuous cell extraction is optimized") {
TEST_DO(verify_compiled("tensor(x[3])(x3y5{x:(x),y:2})", "x3y5{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_compiled("tensor(x[3])(x3y5f{x:(x),y:2})", "x3y5{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_compiled("tensor<float>(x[3])(x3y5{x:(x),y:2})", "x3y5f{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_compiled("tensor<float>(x[3])(x3y5f{x:(x),y:2})", "x3y5f{y:2}", "f(x)((floor(x)*5)+2)"));
}
TEST("require that out-of-bounds cell extraction is not optimized") {
TEST_DO(verify_generic("tensor(x[3])(x3y5{x:1,y:(x+3)})", "tensor(x[3]):[9,10,0]"));
TEST_DO(verify_generic("tensor(x[3])(x3y5{x:1,y:(x-1)})", "tensor(x[3]):[0,6,7]"));
TEST_DO(verify_generic("tensor(x[3])(x3y5{x:(x+1),y:(x)})", "tensor(x[3]):[6,12,0]"));
TEST_DO(verify_generic("tensor(x[3])(x3y5{x:(x-1),y:(x)})", "tensor(x[3]):[0,2,8]"));
}
TEST("require that non-double result from inner tensor lambda function fails type resolving") {
auto fun_a = Function::parse("tensor(x[2])(a)");
auto fun_b = Function::parse("tensor(x[2])(a{y:(x)})");
NodeTypes types_ad(*fun_a, {ValueType::from_spec("double")});
NodeTypes types_at(*fun_a, {ValueType::from_spec("tensor(y[2])")});
NodeTypes types_bd(*fun_b, {ValueType::from_spec("double")});
NodeTypes types_bt(*fun_b, {ValueType::from_spec("tensor(y[2])")});
EXPECT_EQUAL(types_ad.get_type(fun_a->root()).to_spec(), "tensor(x[2])");
EXPECT_EQUAL(types_at.get_type(fun_a->root()).to_spec(), "error");
EXPECT_EQUAL(types_bd.get_type(fun_b->root()).to_spec(), "error");
EXPECT_EQUAL(types_bt.get_type(fun_b->root()).to_spec(), "tensor(x[2])");
}
TEST("require that type resolving also include nodes in the inner tensor lambda function") {
auto fun = Function::parse("tensor(x[2])(a)");
NodeTypes types(*fun, {ValueType::from_spec("double")});
auto lambda = nodes::as<nodes::TensorLambda>(fun->root());
ASSERT_TRUE(lambda != nullptr);
EXPECT_EQUAL(types.get_type(*lambda).to_spec(), "tensor(x[2])");
auto symbol = nodes::as<nodes::Symbol>(lambda->lambda().root());
ASSERT_TRUE(symbol != nullptr);
EXPECT_EQUAL(types.get_type(*symbol).to_spec(), "double");
}
TEST_MAIN() { TEST_RUN_ALL(); }
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