GC leftovers

4 files changed, 0 insertions, 321 deletions

diff --git a/eval/src/tests/tensor/dense_dimension_combiner/CMakeLists.txt b/eval/src/tests/tensor/dense_dimension_combiner/CMakeLists.txt
deleted file mode 100644
index eaee8ebb4e4..00000000000
--- a/eval/src/tests/tensor/dense_dimension_combiner/CMakeLists.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-vespa_add_executable(eval_dense_dimension_combiner_test_app TEST
-    SOURCES
-    dense_dimension_combiner_test.cpp
-    DEPENDS
-    vespaeval
-)
-vespa_add_test(NAME eval_dense_dimension_combiner_test_app COMMAND eval_dense_dimension_combiner_test_app)
diff --git a/eval/src/tests/tensor/dense_dimension_combiner/dense_dimension_combiner_test.cpp b/eval/src/tests/tensor/dense_dimension_combiner/dense_dimension_combiner_test.cpp
deleted file mode 100644
index b8949e3a7e6..00000000000
--- a/eval/src/tests/tensor/dense_dimension_combiner/dense_dimension_combiner_test.cpp
+++ /dev/null
@@ -1,185 +0,0 @@
-// Copyright 2019 Oath Inc. 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/tensor/dense/dense_dimension_combiner.h>
-
-using namespace vespalib;
-using namespace vespalib::eval;
-using namespace vespalib::tensor;
-
-void verifyLeft(DenseDimensionCombiner &d, size_t last) {
-    d.commonReset();
-    d.leftReset();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_EQUAL(d.leftIdx(), 0u);
-    size_t expect = 0;
-    while (d.leftInRange()) {
-        d.stepLeft();
-        EXPECT_GREATER(d.leftIdx(), expect);
-        expect = d.leftIdx();
-    }
-    EXPECT_FALSE(d.leftInRange());
-    EXPECT_EQUAL(expect, last);
-    d.leftReset();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_EQUAL(d.leftIdx(), 0u);
-}
-
-void verifyRight(DenseDimensionCombiner &d, size_t last) {
-    d.commonReset();
-    d.rightReset();
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_EQUAL(d.rightIdx(), 0u);
-    size_t expect = 0;
-    while (d.rightInRange()) {
-        d.stepRight();
-        EXPECT_GREATER(d.rightIdx(), expect);
-        expect = d.rightIdx();
-    }
-    EXPECT_FALSE(d.rightInRange());
-    EXPECT_EQUAL(expect, last);
-    d.rightReset();
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_EQUAL(d.rightIdx(), 0u);
-}
-
-
-TEST("require that one left, one common, one right dimension works") {
-    ValueType t12_lc = ValueType::tensor_type({{"d1_l", 3},{"d2_c", 4}});
-    ValueType t23_cr = ValueType::tensor_type({{"d2_c", 4},{"d3_r", 5}});
-
-    DenseDimensionCombiner d(t12_lc, t23_cr);
-
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 0u);
-    EXPECT_EQUAL(d.rightIdx(), 0u);
-    EXPECT_EQUAL(d.outputIdx(), 0u);
-
-    d.stepCommon();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 1u);
-    EXPECT_EQUAL(d.rightIdx(), 5u);
-    EXPECT_EQUAL(d.outputIdx(), 5u);
-
-    d.stepRight();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 1u);
-    EXPECT_EQUAL(d.rightIdx(), 6u);
-    EXPECT_EQUAL(d.outputIdx(), 6u);
-
-    d.stepLeft();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 5u);
-    EXPECT_EQUAL(d.rightIdx(), 6u);
-    EXPECT_EQUAL(d.outputIdx(), 26u);
-
-    d.stepLeft();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 9u);
-    EXPECT_EQUAL(d.rightIdx(), 6u);
-    EXPECT_EQUAL(d.outputIdx(), 46u);
-
-    d.stepLeft();
-    EXPECT_FALSE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 13u);
-    EXPECT_EQUAL(d.rightIdx(), 6u);
-    EXPECT_EQUAL(d.outputIdx(), 6u);
-
-    d.leftReset();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 1u);
-    EXPECT_EQUAL(d.rightIdx(), 6u);
-    EXPECT_EQUAL(d.outputIdx(), 6u);
-
-    d.stepCommon();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 2u);
-    EXPECT_EQUAL(d.rightIdx(), 11u);
-    EXPECT_EQUAL(d.outputIdx(), 11u);
-
-    d.stepRight();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 2u);
-    EXPECT_EQUAL(d.rightIdx(), 12u);
-    EXPECT_EQUAL(d.outputIdx(), 12u);
-
-    TEST_DO(verifyLeft(d, 12));
-    TEST_DO(verifyRight(d, 20));
-}
-
-TEST("require that two left, no common, two right dimensions works") {
-    ValueType t12_ll = ValueType::tensor_type({{"d1_l", 3},{"d2_l", 4}});
-    ValueType t34_rr = ValueType::tensor_type({{"d3_r", 5},{"d4_r", 2}});
-
-    DenseDimensionCombiner d(t12_ll, t34_rr);
-
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 0u);
-    EXPECT_EQUAL(d.rightIdx(), 0u);
-    EXPECT_EQUAL(d.outputIdx(), 0u);
-
-    d.stepCommon();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_FALSE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 0u);
-    EXPECT_EQUAL(d.rightIdx(), 0u);
-    EXPECT_EQUAL(d.outputIdx(), 120u);
-
-    d.commonReset();
-    d.stepRight();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 0u);
-    EXPECT_EQUAL(d.rightIdx(), 1u);
-    EXPECT_EQUAL(d.outputIdx(), 1u);
-
-    d.stepLeft();
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 1u);
-    EXPECT_EQUAL(d.rightIdx(), 1u);
-    EXPECT_EQUAL(d.outputIdx(), 11u);
-
-    d.stepLeft();
-    d.stepLeft();
-    d.stepLeft();
-    d.stepLeft();
-    d.stepLeft();
-    d.stepLeft();
-    d.stepLeft();
-
-    EXPECT_TRUE(d.leftInRange());
-    EXPECT_TRUE(d.rightInRange());
-    EXPECT_TRUE(d.commonInRange());
-    EXPECT_EQUAL(d.leftIdx(), 8u);
-    EXPECT_EQUAL(d.rightIdx(), 1u);
-    EXPECT_EQUAL(d.outputIdx(), 81u);
-
-    TEST_DO(verifyLeft(d, 12));
-    TEST_DO(verifyRight(d, 10));
-}
-
-TEST_MAIN() { TEST_RUN_ALL(); }
diff --git a/eval/src/tests/tensor/direct_sparse_tensor_builder/CMakeLists.txt b/eval/src/tests/tensor/direct_sparse_tensor_builder/CMakeLists.txt
deleted file mode 100644
index 00ff230fadd..00000000000
--- a/eval/src/tests/tensor/direct_sparse_tensor_builder/CMakeLists.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-vespa_add_executable(eval_direct_sparse_tensor_builder_test_app TEST
-    SOURCES
-    direct_sparse_tensor_builder_test.cpp
-    DEPENDS
-    vespaeval
-)
-vespa_add_test(NAME eval_direct_sparse_tensor_builder_test_app COMMAND eval_direct_sparse_tensor_builder_test_app)
diff --git a/eval/src/tests/tensor/direct_sparse_tensor_builder/direct_sparse_tensor_builder_test.cpp b/eval/src/tests/tensor/direct_sparse_tensor_builder/direct_sparse_tensor_builder_test.cpp
deleted file mode 100644
index bcee6471f76..00000000000
--- a/eval/src/tests/tensor/direct_sparse_tensor_builder/direct_sparse_tensor_builder_test.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-// Copyright 2017 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/tensor/sparse/direct_sparse_tensor_builder.h>
-#include <vespa/eval/tensor/sparse/sparse_tensor_address_combiner.h>
-#include <vespa/vespalib/test/insertion_operators.h>
-
-using namespace vespalib::tensor;
-using namespace vespalib::tensor::sparse;
-using vespalib::eval::TensorSpec;
-using vespalib::eval::CellType;
-using vespalib::eval::ValueType;
-
-void
-assertCellValue(double expValue, const TensorAddress &address,
-                const ValueType &type,
-                const SparseTensor &tensor)
-{
-    SparseTensorAddressBuilder addressBuilder;
-    auto dimsItr = type.dimensions().cbegin();
-    auto dimsItrEnd = type.dimensions().cend();
-    for (const auto &element : address.elements()) {
-        while ((dimsItr < dimsItrEnd) && (dimsItr->name < element.dimension())) {
-            addressBuilder.add("");
-            ++dimsItr;
-        }
-        assert((dimsItr != dimsItrEnd) && (dimsItr->name == element.dimension()));
-        addressBuilder.add(element.label());
-        ++dimsItr;
-    }
-    while (dimsItr < dimsItrEnd) {
-        addressBuilder.add("");
-        ++dimsItr;
-    }
-    SparseTensorAddressRef addressRef(addressBuilder.getAddressRef());
-    size_t idx;
-    bool found = tensor.index().lookup_address(addressRef, idx);
-    EXPECT_TRUE(found);
-    auto cells = tensor.cells();
-    if (EXPECT_TRUE(cells.type == CellType::DOUBLE)) {
-        auto arr = cells.typify<double>();
-        EXPECT_EQUAL(expValue, arr[idx]);
-    }
-}
-
-Tensor::UP
-buildTensor()
-{
-    DirectSparseTensorBuilder<double> builder(ValueType::from_spec("tensor(a{},b{},c{},d{})"));
-    SparseTensorAddressBuilder address;
-    address.set({"1", "2", "", ""});
-    builder.insertCell(address, 10);
-    address.set({"", "", "3", "4"});
-    builder.insertCell(address, 20);
-    return builder.build();
-}
-
-TEST("require that tensor can be constructed")
-{
-    Tensor::UP tensor = buildTensor();
-    const SparseTensor &sparseTensor = dynamic_cast<const SparseTensor &>(*tensor);
-    const ValueType &type = sparseTensor.type();
-    const auto & index = sparseTensor.index();
-    EXPECT_EQUAL(2u, index.size());
-    assertCellValue(10, TensorAddress({{"a","1"},{"b","2"}}), type, sparseTensor);
-    assertCellValue(20, TensorAddress({{"c","3"},{"d","4"}}), type, sparseTensor);
-}
-
-TEST("require that tensor can be converted to tensor spec")
-{
-    Tensor::UP tensor = buildTensor();
-    TensorSpec expSpec("tensor(a{},b{},c{},d{})");
-    expSpec.add({{"a", "1"}, {"b", "2"}, {"c", ""}, {"d", ""}}, 10).
-        add({{"a", ""},{"b",""},{"c", "3"}, {"d", "4"}}, 20);
-    TensorSpec actSpec = tensor->toSpec();
-    EXPECT_EQUAL(expSpec, actSpec);
-}
-
-TEST("require that dimensions are extracted")
-{
-    Tensor::UP tensor = buildTensor();
-    const SparseTensor &sparseTensor = dynamic_cast<const SparseTensor &>(*tensor);
-    const auto &dims = sparseTensor.type().dimensions();
-    EXPECT_EQUAL(4u, dims.size());
-    EXPECT_EQUAL("a", dims[0].name);
-    EXPECT_EQUAL("b", dims[1].name);
-    EXPECT_EQUAL("c", dims[2].name);
-    EXPECT_EQUAL("d", dims[3].name);
-    EXPECT_EQUAL("tensor(a{},b{},c{},d{})", sparseTensor.type().to_spec());
-}
-
-void verifyAddressCombiner(const ValueType & a, const ValueType & b, size_t numDim, size_t numOverlapping) {
-    TensorAddressCombiner combiner(a, b);
-    EXPECT_EQUAL(numDim, combiner.numDimensions());
-    EXPECT_EQUAL(numOverlapping, combiner.numOverlappingDimensions());
-}
-TEST("Test sparse tensor address combiner") {
-    verifyAddressCombiner(ValueType::tensor_type({{"a"}}), ValueType::tensor_type({{"b"}}), 2, 0);
-    verifyAddressCombiner(ValueType::tensor_type({{"a"}, {"b"}}), ValueType::tensor_type({{"b"}}), 2, 1);
-    verifyAddressCombiner(ValueType::tensor_type({{"a"}, {"b"}}), ValueType::tensor_type({{"b"}, {"c"}}), 3, 1);
-
-}
-
-TEST("Test essential object sizes") {
-    EXPECT_EQUAL(16u, sizeof(SparseTensorAddressRef));
-    EXPECT_EQUAL(24u, sizeof(std::pair<SparseTensorAddressRef, double>));
-    EXPECT_EQUAL(32u, sizeof(vespalib::hash_node<std::pair<SparseTensorAddressRef, double>>));
-    Tensor::UP tensor = buildTensor();
-    size_t used = tensor->get_memory_usage().usedBytes();
-    EXPECT_GREATER(used, sizeof(SparseTensor));
-    EXPECT_LESS(used, 10000u);
-    size_t allocated = tensor->get_memory_usage().allocatedBytes();
-    EXPECT_GREATER(allocated, used);
-    EXPECT_LESS(allocated, 50000u);
-    fprintf(stderr, "tensor using %zu bytes of %zu allocated\n",
-            used, allocated);
-}
-
-TEST_MAIN() { TEST_RUN_ALL(); }