1 files changed, 123 insertions, 0 deletions
diff --git a/eval/src/tests/tensor/dense_single_reduce_function/dense_single_reduce_function_test.cpp b/eval/src/tests/tensor/dense_single_reduce_function/dense_single_reduce_function_test.cpp
new file mode 100644
index 00000000000..949c5277e18
--- /dev/null
+++ b/eval/src/tests/tensor/dense_single_reduce_function/dense_single_reduce_function_test.cpp
@@ -0,0 +1,123 @@
+// 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/operation.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_single_reduce_function.h>
+#include <vespa/eval/tensor/dense/dense_tensor.h>
+#include <vespa/eval/tensor/dense/dense_tensor_view.h>
+#include <vespa/eval/eval/test/tensor_model.hpp>
+#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::tensor;
+using namespace vespalib::eval::tensor_function;
+
+const TensorEngine &prod_engine = DefaultTensorEngine::ref();
+
+EvalFixture::ParamRepo make_params() {
+    return EvalFixture::ParamRepo()
+        .add_dense({{"a", 2}, {"b", 3}, {"c", 4}, {"d", 5}})
+        .add_cube("a", 2, "b", 1, "c", 1)
+        .add_cube("a", 1, "b", 2, "c", 1)
+        .add_cube("a", 1, "b", 1, "c", 2)
+        .add_cube("a", 1, "b", 1, "c", 1)
+        .add_vector("a", 10)
+        .add("xy_mapped", spec({x({"a", "b"}),y({"x", "y"})}, N()))
+        .add("xyz_mixed", spec({x({"a", "b"}),y({"x", "y"}),z(3)}, N()));
+}
+EvalFixture::ParamRepo param_repo = make_params();
+
+void verify_optimized(const vespalib::string &expr, size_t dim_idx, Aggr aggr)
+{
+    EvalFixture slow_fixture(prod_engine, expr, param_repo, false);
+    EvalFixture fixture(prod_engine, expr, param_repo, true);
+    EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
+    EXPECT_EQUAL(fixture.result(), slow_fixture.result());
+    auto info = fixture.find_all<DenseSingleReduceFunction>();
+    ASSERT_EQUAL(info.size(), 1u);
+    EXPECT_TRUE(info[0]->result_is_mutable());
+    EXPECT_EQUAL(info[0]->dim_idx(), dim_idx);
+    EXPECT_EQUAL(int(info[0]->aggr()), int(aggr));
+}
+
+void verify_not_optimized(const vespalib::string &expr) {
+    EvalFixture slow_fixture(prod_engine, expr, param_repo, false);
+    EvalFixture fixture(prod_engine, expr, param_repo, true);
+    EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
+    EXPECT_EQUAL(fixture.result(), slow_fixture.result());
+    auto info = fixture.find_all<DenseSingleReduceFunction>();
+    EXPECT_TRUE(info.empty());
+}
+
+TEST("require that multi-dimensional reduce is not optimized") {
+    TEST_DO(verify_not_optimized("reduce(a2b3c4d5,sum,a,b)"));
+    TEST_DO(verify_not_optimized("reduce(a2b3c4d5,sum,c,d)"));
+}
+
+TEST("require that reduce to scalar is not optimized") {
+    TEST_DO(verify_not_optimized("reduce(a10,sum,a)"));
+    TEST_DO(verify_not_optimized("reduce(a10,sum)"));
+}
+
+TEST("require that sparse reduce is not optimized") {
+    TEST_DO(verify_not_optimized("reduce(xy_mapped,sum,x)"));
+    TEST_DO(verify_not_optimized("reduce(xy_mapped,sum,y)"));
+}
+
+TEST("require that mixed reduce is not optimized") {
+    TEST_DO(verify_not_optimized("reduce(xyz_mixed,sum,x)"));
+    TEST_DO(verify_not_optimized("reduce(xyz_mixed,sum,y)"));
+    TEST_DO(verify_not_optimized("reduce(xyz_mixed,sum,z)"));
+}
+
+// NB: these are shadowed by the remove dimension optimization
+TEST("require that reducing self-aggregating trivial dimensions is not optimized") {
+    TEST_DO(verify_not_optimized("reduce(a1b1c1,avg,c)"));
+    TEST_DO(verify_not_optimized("reduce(a1b1c1,prod,c)"));
+    TEST_DO(verify_not_optimized("reduce(a1b1c1,sum,c)"));
+    TEST_DO(verify_not_optimized("reduce(a1b1c1,max,c)"));
+    TEST_DO(verify_not_optimized("reduce(a1b1c1,min,c)"));
+}
+
+TEST("require that reducing trivial dimension with COUNT is 'optimized'") {
+    TEST_DO(verify_optimized("reduce(a1b1c1,count,a)", 0, Aggr::COUNT));
+    TEST_DO(verify_optimized("reduce(a1b1c1,count,b)", 1, Aggr::COUNT));
+    TEST_DO(verify_optimized("reduce(a1b1c1,count,c)", 2, Aggr::COUNT));
+}
+
+vespalib::string make_expr(const vespalib::string &arg, const vespalib::string &dim, bool float_cells, Aggr aggr) {
+    return make_string("reduce(%s%s,%s,%s)", arg.c_str(), float_cells ? "f" : "", AggrNames::name_of(aggr)->c_str(), dim.c_str());
+}
+
+void verify_optimized_multi(const vespalib::string &arg, const vespalib::string &dim, size_t dim_idx) {
+    for (bool float_cells: {false, true}) {
+        for (Aggr aggr: Aggregator::list()) {
+            auto expr = make_expr(arg, dim, float_cells, aggr);
+            TEST_DO(verify_optimized(expr, dim_idx, aggr));
+        }
+    }
+}
+
+TEST("require that normal dense single reduce works") {
+    TEST_DO(verify_optimized_multi("a2b3c4d5", "a", 0));
+    TEST_DO(verify_optimized_multi("a2b3c4d5", "b", 1));
+    TEST_DO(verify_optimized_multi("a2b3c4d5", "c", 2));
+    TEST_DO(verify_optimized_multi("a2b3c4d5", "d", 3));
+}
+
+TEST("require that minimal dense single reduce works") {
+    TEST_DO(verify_optimized_multi("a2b1c1", "a", 0));
+    TEST_DO(verify_optimized_multi("a1b2c1", "b", 1));
+    TEST_DO(verify_optimized_multi("a1b1c2", "c", 2));
+}
+
+TEST_MAIN() { TEST_RUN_ALL(); }