move generic join into its own files

2 files changed, 128 insertions, 0 deletions

diff --git a/eval/src/tests/instruction/generic_join/CMakeLists.txt b/eval/src/tests/instruction/generic_join/CMakeLists.txt
new file mode 100644
index 00000000000..13fc6550d3c
--- /dev/null
+++ b/eval/src/tests/instruction/generic_join/CMakeLists.txt
@@ -0,0 +1,9 @@
+# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+vespa_add_executable(eval_generic_join_test_app TEST
+    SOURCES
+    generic_join_test.cpp
+    DEPENDS
+    vespaeval
+    GTest::GTest
+)
+vespa_add_test(NAME eval_generic_join_test_app COMMAND eval_generic_join_test_app)
diff --git a/eval/src/tests/instruction/generic_join/generic_join_test.cpp b/eval/src/tests/instruction/generic_join/generic_join_test.cpp
new file mode 100644
index 00000000000..53df23d77be
--- /dev/null
+++ b/eval/src/tests/instruction/generic_join/generic_join_test.cpp
@@ -0,0 +1,119 @@
+// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+#include <vespa/eval/eval/simple_value.h>
+#include <vespa/eval/eval/value_codec.h>
+#include <vespa/eval/instruction/generic_join.h>
+#include <vespa/eval/eval/interpreted_function.h>
+#include <vespa/eval/eval/test/tensor_model.hpp>
+#include <vespa/vespalib/util/stringfmt.h>
+#include <vespa/vespalib/gtest/gtest.h>
+
+using namespace vespalib;
+using namespace vespalib::eval;
+using namespace vespalib::eval::instruction;
+using namespace vespalib::eval::test;
+
+using vespalib::make_string_short::fmt;
+
+std::vector<Layout> join_layouts = {
+    {},                                                 {},
+    {x(5)},                                             {x(5)},
+    {x(5)},                                             {y(5)},
+    {x(5)},                                             {x(5),y(5)},
+    {y(3)},                                             {x(2),z(3)},
+    {x(3),y(5)},                                        {y(5),z(7)},
+    float_cells({x(3),y(5)}),                           {y(5),z(7)},
+    {x(3),y(5)},                                        float_cells({y(5),z(7)}),
+    float_cells({x(3),y(5)}),                           float_cells({y(5),z(7)}),
+    {x({"a","b","c"})},                                 {x({"a","b","c"})},
+    {x({"a","b","c"})},                                 {x({"a","b"})},
+    {x({"a","b","c"})},                                 {y({"foo","bar","baz"})},
+    {x({"a","b","c"})},                                 {x({"a","b","c"}),y({"foo","bar","baz"})},
+    {x({"a","b"}),y({"foo","bar","baz"})},              {x({"a","b","c"}),y({"foo","bar"})},
+    {x({"a","b"}),y({"foo","bar","baz"})},              {y({"foo","bar"}),z({"i","j","k","l"})},
+    float_cells({x({"a","b"}),y({"foo","bar","baz"})}), {y({"foo","bar"}),z({"i","j","k","l"})},
+    {x({"a","b"}),y({"foo","bar","baz"})},              float_cells({y({"foo","bar"}),z({"i","j","k","l"})}),
+    float_cells({x({"a","b"}),y({"foo","bar","baz"})}), float_cells({y({"foo","bar"}),z({"i","j","k","l"})}),
+    {x(3),y({"foo", "bar"})},                           {y({"foo", "bar"}),z(7)},
+    {x({"a","b","c"}),y(5)},                            {y(5),z({"i","j","k","l"})},
+    float_cells({x({"a","b","c"}),y(5)}),               {y(5),z({"i","j","k","l"})},
+    {x({"a","b","c"}),y(5)},                            float_cells({y(5),z({"i","j","k","l"})}),
+    float_cells({x({"a","b","c"}),y(5)}),               float_cells({y(5),z({"i","j","k","l"})})
+};
+
+TensorSpec simple_tensor_join(const TensorSpec &a, const TensorSpec &b, join_fun_t function) {
+    Stash stash;
+    const auto &engine = SimpleTensorEngine::ref();
+    auto lhs = engine.from_spec(a);
+    auto rhs = engine.from_spec(b);
+    const auto &result = engine.join(*lhs, *rhs, function, stash);
+    return engine.to_spec(result);
+}
+
+TensorSpec perform_generic_join(const TensorSpec &a, const TensorSpec &b, join_fun_t function) {
+    Stash stash;
+    const auto &factory = SimpleValueBuilderFactory::get();
+    auto lhs = value_from_spec(a, factory);
+    auto rhs = value_from_spec(b, factory);
+    auto my_op = GenericJoin::make_instruction(lhs->type(), rhs->type(), function, factory, stash);
+    InterpretedFunction::EvalSingle single(my_op);
+    return spec_from_value(single.eval(std::vector<Value::CREF>({*lhs,*rhs})));
+}
+
+TEST(GenericJoinTest, dense_join_plan_can_be_created) {
+    auto lhs = ValueType::from_spec("tensor(a{},b[6],c[5],e[3],f[2],g{})");
+    auto rhs = ValueType::from_spec("tensor(a{},b[6],c[5],d[4],h{})");
+    auto plan = DenseJoinPlan(lhs, rhs);
+    std::vector<size_t> expect_loop = {30,4,6};
+    std::vector<size_t> expect_lhs_stride = {6,0,1};
+    std::vector<size_t> expect_rhs_stride = {4,1,0};
+    EXPECT_EQ(plan.lhs_size, 180);
+    EXPECT_EQ(plan.rhs_size, 120);
+    EXPECT_EQ(plan.out_size, 720);
+    EXPECT_EQ(plan.loop_cnt, expect_loop);
+    EXPECT_EQ(plan.lhs_stride, expect_lhs_stride);
+    EXPECT_EQ(plan.rhs_stride, expect_rhs_stride);
+}
+
+TEST(GenericJoinTest, sparse_join_plan_can_be_created) {
+    auto lhs = ValueType::from_spec("tensor(a{},b[6],c[5],e[3],f[2],g{})");
+    auto rhs = ValueType::from_spec("tensor(b[6],c[5],d[4],g{},h{})");
+    auto plan = SparseJoinPlan(lhs, rhs);
+    using SRC = SparseJoinPlan::Source;
+    std::vector<SRC> expect_sources = {SRC::LHS,SRC::BOTH,SRC::RHS};
+    std::vector<size_t> expect_lhs_overlap = {1};
+    std::vector<size_t> expect_rhs_overlap = {0};
+    EXPECT_EQ(plan.sources, expect_sources);
+    EXPECT_EQ(plan.lhs_overlap, expect_lhs_overlap);
+    EXPECT_EQ(plan.rhs_overlap, expect_rhs_overlap);
+}
+
+TEST(GenericJoinTest, dense_join_plan_can_be_executed) {
+    auto plan = DenseJoinPlan(ValueType::from_spec("tensor(a[2])"),
+                              ValueType::from_spec("tensor(b[3])"));
+    std::vector<int> a({1, 2});
+    std::vector<int> b({3, 4, 5});
+    std::vector<int> c(6, 0);
+    std::vector<int> expect = {3,4,5,6,8,10};
+    ASSERT_EQ(plan.out_size, 6);
+    int *dst = &c[0];
+    auto cell_join = [&](size_t a_idx, size_t b_idx) { *dst++ = (a[a_idx] * b[b_idx]); };
+    plan.execute(0, 0, cell_join);
+    EXPECT_EQ(c, expect);
+}
+
+TEST(GenericJoinTest, generic_join_works_for_simple_values) {
+    ASSERT_TRUE((join_layouts.size() % 2) == 0);
+    for (size_t i = 0; i < join_layouts.size(); i += 2) {
+        TensorSpec lhs = spec(join_layouts[i], Div16(N()));
+        TensorSpec rhs = spec(join_layouts[i + 1], Div16(N()));
+        for (auto fun: {operation::Add::f, operation::Sub::f, operation::Mul::f, operation::Div::f}) {
+            SCOPED_TRACE(fmt("\n===\nLHS: %s\nRHS: %s\n===\n", lhs.to_string().c_str(), rhs.to_string().c_str()));
+            auto expect = simple_tensor_join(lhs, rhs, fun);
+            auto actual = perform_generic_join(lhs, rhs, fun);
+            EXPECT_EQ(actual, expect);
+        }
+    }
+}
+
+GTEST_MAIN_RUN_ALL_TESTS()