test result mutability of optimized operations

also clean up vector from doubles test using eval fixture

3 files changed, 54 insertions, 131 deletions

diff --git a/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp b/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp
index fb48e445180..37f9602565d 100644
--- a/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp
+++ b/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp
@@ -131,7 +131,8 @@ void assertOptimized(const vespalib::string &expr) {
     EvalFixture fixture(prod_engine, expr, param_repo, true);
     EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
     auto info = fixture.find_all<DenseDotProductFunction>();
-    EXPECT_EQUAL(info.size(), 1u);
+    ASSERT_EQUAL(info.size(), 1u);
+    EXPECT_TRUE(info[0]->result_is_mutable());
 }
 
 void assertNotOptimized(const vespalib::string &expr) {
diff --git a/eval/src/tests/tensor/dense_fast_rename_function/dense_fast_rename_function_test.cpp b/eval/src/tests/tensor/dense_fast_rename_function/dense_fast_rename_function_test.cpp
index fab16f1e276..45b38b48481 100644
--- a/eval/src/tests/tensor/dense_fast_rename_function/dense_fast_rename_function_test.cpp
+++ b/eval/src/tests/tensor/dense_fast_rename_function/dense_fast_rename_function_test.cpp
@@ -24,17 +24,19 @@ const TensorEngine &prod_engine = DefaultTensorEngine::ref();
 EvalFixture::ParamRepo make_params() {
     return EvalFixture::ParamRepo()
         .add("x5", spec({x(5)}, N()))
+        .add_mutable("mut_x5", spec({x(5)}, N()))
         .add("x5_u", spec({x(5)}, N()), "tensor(x[])")
         .add("x_m", spec({x({"a", "b", "c"})}, N()))
         .add("x5y3", spec({x(5),y(3)}, N()));
 }
 EvalFixture::ParamRepo param_repo = make_params();
 
-void verify_optimized(const vespalib::string &expr) {
-    EvalFixture fixture(prod_engine, expr, param_repo, true);
+void verify_optimized(const vespalib::string &expr, bool expect_mutable = false) {
+    EvalFixture fixture(prod_engine, expr, param_repo, true, true);
     EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
     auto info = fixture.find_all<DenseFastRenameFunction>();
-    EXPECT_EQUAL(info.size(), 1u);
+    ASSERT_EQUAL(info.size(), 1u);
+    EXPECT_EQUAL(info[0]->result_is_mutable(), expect_mutable);
 }
 
 void verify_not_optimized(const vespalib::string &expr) {
@@ -71,4 +73,25 @@ TEST("require that non-dense renames are not optimized") {
     TEST_DO(verify_not_optimized("rename(x_m,x,y)"));
 }
 
+TEST("require that renaming a mutable result retains mutability") {
+    TEST_DO(verify_optimized("rename(mut_x5,x,y)", true));
+}
+
+TEST("require that child mutability changed under-the-hood is still reflected") {
+    Stash stash;
+    const Node &a = inject(ValueType::from_spec("tensor(x[2])"), 0, stash);
+    const Node &tmp = map(a, operation::Neg::f, stash); // will be mutable
+    DenseFastRenameFunction my_rename(ValueType::from_spec("tensor(y[2])"), a);
+    EXPECT_TRUE(!my_rename.result_is_mutable());
+    {
+        std::vector<TensorFunction::Child::CREF> list;
+        my_rename.push_children(list);
+        ASSERT_EQUAL(list.size(), 1u);
+        EXPECT_EQUAL(&(list[0].get().get()), &a);
+        const TensorFunction::Child &child = list[0];
+        child.set(tmp);
+    }
+    EXPECT_TRUE(my_rename.result_is_mutable());
+}
+
 TEST_MAIN() { TEST_RUN_ALL(); }
diff --git a/eval/src/tests/tensor/vector_from_doubles_function/vector_from_doubles_function_test.cpp b/eval/src/tests/tensor/vector_from_doubles_function/vector_from_doubles_function_test.cpp
index 0ba9871d672..41d98122e0f 100644
--- a/eval/src/tests/tensor/vector_from_doubles_function/vector_from_doubles_function_test.cpp
+++ b/eval/src/tests/tensor/vector_from_doubles_function/vector_from_doubles_function_test.cpp
@@ -1,164 +1,63 @@
 // Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
 
-#include <vespa/log/log.h>
-LOG_SETUP("dense_dot_product_function_test");
-
 #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/vector_from_doubles_function.h>
 #include <vespa/eval/tensor/dense/dense_tensor.h>
-#include <vespa/eval/tensor/dense/dense_tensor_builder.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 &ref_engine = SimpleTensorEngine::ref();
 const TensorEngine &prod_engine = DefaultTensorEngine::ref();
 
-//-----------------------------------------------------------------------------
-// verify that optimize() works as expected
-
-template<typename OPT>
-bool treeContains(const TensorFunction &expr) {
-    using Child = TensorFunction::Child;
-    Child root(expr);
-    std::vector<Child::CREF> nodes({root});
-    for (size_t i = 0; i < nodes.size(); ++i) {
-        nodes[i].get().get().push_children(nodes);
-    }
-    for (const Child &child : nodes) {
-        if (as<OPT>(child.get())) {
-            return true;
-        }
-    }
-    return false;
-}
-
-const TensorFunction &optimize_fun(const Function &fun, const NodeTypes &node_types, Stash &stash) {
-    const TensorFunction &plain_fun = make_tensor_function(prod_engine, fun.root(), node_types, stash);
-    return prod_engine.optimize(plain_fun, stash);
+EvalFixture::ParamRepo make_params() {
+    return EvalFixture::ParamRepo()
+        .add("a", spec(1.0))
+        .add("b", spec(2.0))
+        .add("c", spec(3.0))
+        .add("d", spec(4.0))
+        .add("x5", spec({x(5)}, N()));
 }
+EvalFixture::ParamRepo param_repo = make_params();
 
-std::vector<ValueType> extract_types(size_t n, const std::vector<TensorSpec> &input) {
-    std::vector<ValueType> vec;
-    for (const TensorSpec &spec : input) {
-        vec.push_back(ValueType::from_spec(spec.type()));
-    }
-    while (vec.size() < n) {
-        vec.push_back(ValueType::double_type());
+void verify(const vespalib::string &expr, size_t expect_optimized_cnt, size_t expect_not_optimized_cnt) {
+    EvalFixture fixture(prod_engine, expr, param_repo, true);
+    EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo));
+    auto info = fixture.find_all<VectorFromDoublesFunction>();
+    EXPECT_EQUAL(info.size(), expect_optimized_cnt);
+    for (size_t i = 0; i < info.size(); ++i) {
+        EXPECT_TRUE(info[i]->result_is_mutable());
     }
-    return vec;
+    EXPECT_EQUAL(fixture.find_all<Concat>().size(), expect_not_optimized_cnt);
 }
 
-struct Context {
-    Stash stash;
-    Function function;
-    std::vector<TensorSpec> input;
-    std::vector<ValueType> input_types;
-    NodeTypes node_types;
-    const TensorFunction &optimized;
-
-    Context(const vespalib::string &expr, std::vector<TensorSpec> in)
-        : stash(),
-          function(Function::parse(expr)),
-          input(in),
-          input_types(extract_types(function.num_params(), input)),
-          node_types(function, input_types),
-          optimized(optimize_fun(function, node_types, stash))
-    {
-        EXPECT_EQUAL(actual(), expected());
-    }
-
-    ~Context() {}
-
-    struct Params : LazyParams {
-        std::vector<Value::UP> values;
-        Value &resolve(size_t idx, Stash &) const override {
-            return *values[idx];
-        }
-    };
-
-    Params gen_params(const TensorEngine &engine) {
-        Params p;
-        for (const TensorSpec &spec : input) {
-            p.values.emplace_back(engine.from_spec(spec));
-        }
-        while (p.values.size() < function.num_params()) {
-            double v = 1.0 + p.values.size();
-            p.values.emplace_back(std::make_unique<DoubleValue>(v));
-        }
-        return p;
-    }
-
-    TensorSpec actual() {
-        const LazyParams &params = gen_params(prod_engine);
-        InterpretedFunction prodIfun(prod_engine, optimized);
-        InterpretedFunction::Context prodIctx(prodIfun);
-        const Value &result = prodIfun.eval(prodIctx, params);
-        return prod_engine.to_spec(result);
-    }
-
-    TensorSpec expected() {
-        const LazyParams &params = gen_params(ref_engine);
-        InterpretedFunction refIfun(ref_engine, function, NodeTypes());
-        InterpretedFunction::Context refIctx(refIfun);
-        const Value &result = refIfun.eval(refIctx, params);
-        return ref_engine.to_spec(result);
-    }
-
-};
-
 //-----------------------------------------------------------------------------
 
-void verify_all_optimized(const vespalib::string &expr) {
-    Context context(expr, {});
-    EXPECT_TRUE(treeContains<VectorFromDoublesFunction>(context.optimized));
-    EXPECT_FALSE(treeContains<eval::tensor_function::Concat>(context.optimized));
-}
-
 TEST("require that multiple concats are optimized") {
-    TEST_DO(verify_all_optimized("concat(a,b,x)"));
-    TEST_DO(verify_all_optimized("concat(a,concat(b,concat(c,d,x),x),x)"));
-    TEST_DO(verify_all_optimized("concat(concat(concat(a,b,x),c,x),d,x)"));
-    TEST_DO(verify_all_optimized("concat(concat(a,b,x),concat(c,d,x),x)"));
-}
-
-//-----------------------------------------------------------------------------
-
-void verify_some_optimized(const vespalib::string &expr) {
-    Context context(expr, {});
-    EXPECT_TRUE(treeContains<VectorFromDoublesFunction>(context.optimized));
-    EXPECT_TRUE(treeContains<eval::tensor_function::Concat>(context.optimized));
+    TEST_DO(verify("concat(a,b,x)", 1, 0));
+    TEST_DO(verify("concat(a,concat(b,concat(c,d,x),x),x)", 1, 0));
+    TEST_DO(verify("concat(concat(concat(a,b,x),c,x),d,x)", 1, 0));
+    TEST_DO(verify("concat(concat(a,b,x),concat(c,d,x),x)", 1, 0));
 }
 
 TEST("require that concat along different dimension is not optimized") {
-    TEST_DO(verify_some_optimized("concat(concat(a,b,x),concat(c,d,x),y)"));
+    TEST_DO(verify("concat(concat(a,b,x),concat(c,d,x),y)", 2, 1));
 }
 
-//-----------------------------------------------------------------------------
-
 TEST("require that concat of vector and double is not optimized") {
-    TensorSpec vecspec = TensorSpec("tensor(x[3])")
-                         .add({{"x", 0}}, 7.0)
-                         .add({{"x", 1}}, 11.0)
-                         .add({{"x", 2}}, 13.0);
-    TensorSpec dblspec = TensorSpec("double")
-                         .add({}, 19.0);
-    Context context("concat(a,b,x)", {vecspec, dblspec});
-    EXPECT_TRUE(treeContains<eval::tensor_function::Concat>(context.optimized));
-    EXPECT_FALSE(treeContains<VectorFromDoublesFunction>(context.optimized));
+    TEST_DO(verify("concat(a,x5,x)", 0, 1));
+    TEST_DO(verify("concat(x5,b,x)", 0, 1));
 }
 
-//-----------------------------------------------------------------------------
-
 TEST_MAIN() { TEST_RUN_ALL(); }