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// Copyright Vespa.ai. 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_value.h>
#include <vespa/eval/eval/fast_value.h>
#include <vespa/eval/instruction/replace_type_function.h>
#include <vespa/eval/instruction/dense_cell_range_function.h>
#include <vespa/eval/instruction/dense_lambda_peek_function.h>
#include <vespa/eval/instruction/fast_rename_optimizer.h>
#include <vespa/eval/eval/test/gen_spec.h>
#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::eval::tensor_function;
const ValueBuilderFactory &simple_factory = SimpleValueBuilderFactory::get();
const ValueBuilderFactory &prod_factory = FastValueBuilderFactory::get();
EvalFixture::ParamRepo make_params() {
return EvalFixture::ParamRepo()
.add("a", GenSpec(1))
.add("b", GenSpec(2))
.add("x3", GenSpec().idx("x", 3))
.add("x3_float", GenSpec().idx("x", 3).cells(CellType::FLOAT))
.add("x3_bfloat16", GenSpec().idx("x", 3).cells(CellType::BFLOAT16))
.add("x3_int8", GenSpec().idx("x", 3).cells(CellType::INT8))
.add("x3m", GenSpec().map("x", {"0", "1", "2"}))
.add("x3y5", GenSpec().idx("x", 3).idx("y", 5))
.add("x3y5_float", GenSpec().idx("x", 3).idx("y", 5).cells(CellType::FLOAT))
.add("x3y5_bfloat16", GenSpec().idx("x", 3).idx("y", 5).cells(CellType::BFLOAT16))
.add("x3y5_int8", GenSpec().idx("x", 3).idx("y", 5).cells(CellType::INT8))
.add("x15", GenSpec().idx("x", 15))
.add("x15_float", GenSpec().idx("x", 15).cells(CellType::FLOAT))
.add("x15_bfloat16", GenSpec().idx("x", 15).cells(CellType::BFLOAT16))
.add("x15_int8", GenSpec().idx("x", 15).cells(CellType::INT8));
}
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_factory, expr, param_repo, true);
EvalFixture slow_fixture(prod_factory, expr, param_repo, false);
EvalFixture simple_factory_fixture(simple_factory, expr, param_repo, false);
auto expect_spec = EvalFixture::ref(expect, param_repo);
EXPECT_EQUAL(fixture.result(), expect_spec);
EXPECT_EQUAL(slow_fixture.result(), expect_spec);
EXPECT_EQUAL(simple_factory_fixture.result(), expect_spec);
EXPECT_EQUAL(EvalFixture::ref(expr, param_repo), expect_spec);
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<tensor_function::Lambda>(expr, expect);
}
void verify_reshape(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<ReplaceTypeFunction>(expr, expect);
}
void verify_range(const vespalib::string &expr, const vespalib::string &expect) {
verify_impl<DenseCellRangeFunction>(expr, expect);
}
void verify_idx_fun(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 tensor lambda can be used for cell type casting") {
TEST_DO(verify_idx_fun("tensor(x[3])(x3_float{x:(x)})", "tensor(x[3]):[1,2,3]", "f(x)(x)"));
TEST_DO(verify_idx_fun("tensor(x[3])(x3_bfloat16{x:(x)})", "tensor(x[3]):[1,2,3]", "f(x)(x)"));
TEST_DO(verify_idx_fun("tensor(x[3])(x3_int8{x:(x)})", "tensor(x[3]):[1,2,3]", "f(x)(x)"));
TEST_DO(verify_idx_fun("tensor<float>(x[3])(x3{x:(x)})", "tensor<float>(x[3]):[1,2,3]", "f(x)(x)"));
TEST_DO(verify_idx_fun("tensor<bfloat16>(x[3])(x3{x:(x)})", "tensor<bfloat16>(x[3]):[1,2,3]", "f(x)(x)"));
TEST_DO(verify_idx_fun("tensor<int8>(x[3])(x3{x:(x)})", "tensor<int8>(x[3]):[1,2,3]", "f(x)(x)"));
}
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 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])(x3y5_float{x:(x/5),y:(x%5)})", "x15_float"));
TEST_DO(verify_reshape("tensor<bfloat16>(x[15])(x3y5_bfloat16{x:(x/5),y:(x%5)})", "x15_bfloat16"));
TEST_DO(verify_reshape("tensor<int8>(x[15])(x3y5_int8{x:(x/5),y:(x%5)})", "x15_int8"));
}
TEST("require that tensor reshape with non-matching cell type requires cell copy") {
TEST_DO(verify_idx_fun("tensor(x[15])(x3y5_float{x:(x/5),y:(x%5)})", "x15", "f(x)((floor((x/5))*5)+(x%5))"));
TEST_DO(verify_idx_fun("tensor<float>(x[15])(x3y5{x:(x/5),y:(x%5)})", "x15_float", "f(x)((floor((x/5))*5)+(x%5))"));
TEST_DO(verify_idx_fun("tensor(x[3],y[5])(x15_float{x:(x*5+y)})", "x3y5", "f(x,y)((x*5)+y)"));
TEST_DO(verify_idx_fun("tensor<float>(x[3],y[5])(x15{x:(x*5+y)})", "x3y5_float", "f(x,y)((x*5)+y)"));
TEST_DO(verify_idx_fun("tensor<bfloat16>(x[3],y[5])(x15{x:(x*5+y)})", "x3y5_bfloat16", "f(x,y)((x*5)+y)"));
TEST_DO(verify_idx_fun("tensor<int8>(x[3],y[5])(x15{x:(x*5+y)})", "x3y5_int8", "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])(x3y5_float{x:1,y:(y)})", "x3y5_float{x:1}"));
TEST_DO(verify_range("tensor<float>(x[3])(x15_float{x:(x+5)})", "tensor<float>(x[3]):[6,7,8]"));
TEST_DO(verify_range("tensor<float>(x[3])(x15_float{x:(x+5)})", "tensor<float>(x[3]):[6,7,8]"));
TEST_DO(verify_range("tensor<bfloat16>(x[3])(x15_bfloat16{x:(x+5)})", "tensor<bfloat16>(x[3]):[6,7,8]"));
TEST_DO(verify_range("tensor<int8>(x[3])(x15_int8{x:(x+5)})", "tensor<int8>(x[3]):[6,7,8]"));
}
TEST("require that tensor cell subrange with non-matching cell type requires cell copy") {
TEST_DO(verify_idx_fun("tensor(x[3])(x15_float{x:(x+5)})", "tensor(x[3]):[6,7,8]", "f(x)(x+5)"));
TEST_DO(verify_idx_fun("tensor<float>(x[3])(x15{x:(x+5)})", "tensor<float>(x[3]):[6,7,8]", "f(x)(x+5)"));
TEST_DO(verify_idx_fun("tensor<bfloat16>(x[3])(x15{x:(x+5)})", "tensor<bfloat16>(x[3]):[6,7,8]", "f(x)(x+5)"));
TEST_DO(verify_idx_fun("tensor<int8>(x[3])(x15{x:(x+5)})", "tensor<int8>(x[3]):[6,7,8]", "f(x)(x+5)"));
}
TEST("require that non-continuous cell extraction is optimized") {
TEST_DO(verify_idx_fun("tensor(x[3])(x3y5{x:(x),y:2})", "x3y5{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_idx_fun("tensor(x[3])(x3y5_float{x:(x),y:2})", "x3y5{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_idx_fun("tensor<float>(x[3])(x3y5{x:(x),y:2})", "x3y5_float{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_idx_fun("tensor<float>(x[3])(x3y5_float{x:(x),y:2})", "x3y5_float{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_idx_fun("tensor<bfloat16>(x[3])(x3y5_bfloat16{x:(x),y:2})", "x3y5_bfloat16{y:2}", "f(x)((floor(x)*5)+2)"));
TEST_DO(verify_idx_fun("tensor<int8>(x[3])(x3y5_int8{x:(x),y:2})", "x3y5_int8{y:2}", "f(x)((floor(x)*5)+2)"));
}
TEST("require that simple dynamic tensor lambda works") {
TEST_DO(verify_generic("tensor(x[3])(x+a)", "tensor(x[3]):[1,2,3]"));
TEST_DO(verify_generic("tensor<float>(x[3])(x+a)", "tensor<float>(x[3]):[1,2,3]"));
TEST_DO(verify_generic("tensor<bfloat16>(x[3])(x+a)", "tensor<bfloat16>(x[3]):[1,2,3]"));
TEST_DO(verify_generic("tensor<int8>(x[3])(x+a)", "tensor<int8>(x[3]):[1,2,3]"));
}
TEST("require that compiled multi-dimensional multi-param dynamic tensor lambda works") {
TEST_DO(verify_generic("tensor(x[3],y[2])((b-a)+x+y)", "tensor(x[3],y[2]):[[1,2],[2,3],[3,4]]"));
TEST_DO(verify_generic("tensor<float>(x[3],y[2])((b-a)+x+y)", "tensor<float>(x[3],y[2]):[[1,2],[2,3],[3,4]]"));
}
TEST("require that interpreted multi-dimensional multi-param dynamic tensor lambda works") {
TEST_DO(verify_generic("tensor(x[3],y[2])((x3{x:(a)}-a)+x+y)", "tensor(x[3],y[2]):[[1,2],[2,3],[3,4]]"));
TEST_DO(verify_generic("tensor<float>(x[3],y[2])((x3{x:(a)}-a)+x+y)", "tensor<float>(x[3],y[2]):[[1,2],[2,3],[3,4]]"));
}
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 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 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 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");
}
size_t count_nodes(const NodeTypes &types) {
size_t cnt = 0;
types.each([&](const auto &, const auto &){++cnt;});
return cnt;
}
TEST("require that type exporting also include nodes in the inner tensor lambda function") {
auto fun = Function::parse("tensor(x[2])(tensor(y[2])((x+y)+a){y:(x)})");
NodeTypes types(*fun, {ValueType::from_spec("double")});
const auto &root = fun->root();
NodeTypes copy = types.export_types(root);
EXPECT_TRUE(copy.errors().empty());
EXPECT_EQUAL(count_nodes(types), count_nodes(copy));
auto lambda = nodes::as<nodes::TensorLambda>(root);
ASSERT_TRUE(lambda != nullptr);
NodeTypes outer = copy.export_types(lambda->lambda().root());
EXPECT_TRUE(outer.errors().empty());
auto inner_lambda = nodes::as<nodes::TensorLambda>(lambda->lambda().root().get_child(0));
ASSERT_TRUE(inner_lambda != nullptr);
NodeTypes inner = outer.export_types(inner_lambda->lambda().root());
EXPECT_TRUE(inner.errors().empty());
// [x, y, (x+y), a, (x+y)+a] are the 5 nodes:
EXPECT_EQUAL(count_nodes(inner), 5u);
}
TEST_MAIN() { TEST_RUN_ALL(); }
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