diff options
Diffstat (limited to 'eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp')
| -rw-r--r-- | eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp | 223 |
1 files changed, 182 insertions, 41 deletions
diff --git a/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp b/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp index 28a4a34b2e4..db2415e9969 100644 --- a/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp +++ b/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp @@ -10,83 +10,224 @@ using namespace vespalib::eval; using namespace vespalib::tensor; using vespalib::make_string_short::fmt; +using TensorInfo = Onnx::TensorInfo; +using DZ = Onnx::DimSize; std::string get_source_dir() { const char *dir = getenv("SOURCE_DIRECTORY"); return (dir ? dir : "."); } std::string source_dir = get_source_dir(); -std::string vespa_dir = source_dir + "/" + "../../../../.."; -std::string simple_model = vespa_dir + "/" + "model-integration/src/test/models/onnx/simple/simple.onnx"; +std::string simple_model = source_dir + "/simple.onnx"; +std::string dynamic_model = source_dir + "/dynamic.onnx"; -void dump_info(const char *ctx, const std::vector<OnnxWrapper::TensorInfo> &info) { +void dump_info(const char *ctx, const std::vector<TensorInfo> &info) { fprintf(stderr, "%s:\n", ctx); for (size_t i = 0; i < info.size(); ++i) { fprintf(stderr, " %s[%zu]: '%s' %s\n", ctx, i, info[i].name.c_str(), info[i].type_as_string().c_str()); } } -TEST(OnnxWrapperTest, onnx_model_can_be_inspected) +TEST(WirePlannerTest, element_types_must_match) { + Onnx::WirePlanner planner; + ValueType type1 = ValueType::from_spec("tensor<float>(a[5])"); + ValueType type2 = ValueType::from_spec("tensor<double>(a[5])"); + TensorInfo info1 = TensorInfo{"info", {DZ(5)}, TensorInfo::ElementType::FLOAT}; + TensorInfo info2 = TensorInfo{"info", {DZ(5)}, TensorInfo::ElementType::DOUBLE}; + EXPECT_TRUE(planner.bind_input_type(type1, info1)); + EXPECT_FALSE(planner.bind_input_type(type2, info1)); + EXPECT_FALSE(planner.bind_input_type(type1, info2)); + EXPECT_TRUE(planner.bind_input_type(type2, info2)); +} + +TEST(WirePlannerTest, known_dimension_sizes_must_match) { + Onnx::WirePlanner planner; + ValueType type1 = ValueType::from_spec("tensor<float>(a[5],b[10])"); + ValueType type2 = ValueType::from_spec("tensor<float>(a[10],b[5])"); + ValueType type3 = ValueType::from_spec("tensor<float>(a[5],b[5])"); + TensorInfo info = TensorInfo{"info", {DZ(5),DZ(5)}, TensorInfo::ElementType::FLOAT}; + EXPECT_FALSE(planner.bind_input_type(type1, info)); + EXPECT_FALSE(planner.bind_input_type(type2, info)); + EXPECT_TRUE(planner.bind_input_type(type3, info)); +} + +TEST(WirePlannerTest, symbolic_dimension_sizes_must_match) { + Onnx::WirePlanner planner; + ValueType type1 = ValueType::from_spec("tensor<float>(a[5])"); + ValueType type2 = ValueType::from_spec("tensor<float>(a[10])"); + TensorInfo info = TensorInfo{"info", {DZ("dim")}, TensorInfo::ElementType::FLOAT}; + EXPECT_TRUE(planner.bind_input_type(type1, info)); // binds 'dim' to 5 + EXPECT_FALSE(planner.bind_input_type(type2, info)); + EXPECT_TRUE(planner.bind_input_type(type1, info)); +} + +TEST(WirePlannerTest, unknown_dimension_sizes_match_anything) { + Onnx::WirePlanner planner; + ValueType type1 = ValueType::from_spec("tensor<float>(a[5])"); + ValueType type2 = ValueType::from_spec("tensor<float>(a[10])"); + TensorInfo info = TensorInfo{"info", {DZ()}, TensorInfo::ElementType::FLOAT}; + EXPECT_TRUE(planner.bind_input_type(type1, info)); + EXPECT_TRUE(planner.bind_input_type(type2, info)); +} + +TEST(WirePlannerTest, all_output_dimensions_must_be_bound) { + Onnx::WirePlanner planner; + ValueType type = ValueType::from_spec("tensor<float>(a[5],b[10])"); + TensorInfo info1 = TensorInfo{"info", {DZ()}, TensorInfo::ElementType::FLOAT}; + TensorInfo info2 = TensorInfo{"info", {DZ("dim")}, TensorInfo::ElementType::FLOAT}; + TensorInfo info3 = TensorInfo{"info", {DZ("dim"),DZ()}, TensorInfo::ElementType::FLOAT}; + EXPECT_TRUE(planner.make_output_type(info1).is_error()); + EXPECT_TRUE(planner.make_output_type(info2).is_error()); + EXPECT_TRUE(planner.make_output_type(info3).is_error()); + EXPECT_TRUE(planner.bind_input_type(type, info3)); // binds 'dim' to 5 + EXPECT_TRUE(planner.make_output_type(info1).is_error()); + EXPECT_EQ(planner.make_output_type(info2).to_spec(), "tensor<float>(d0[5])"); + EXPECT_TRUE(planner.make_output_type(info3).is_error()); +} + +TEST(WirePlannerTest, dimensions_resolve_left_to_right) { + Onnx::WirePlanner planner; + ValueType type1 = ValueType::from_spec("tensor<float>(a[5],b[10])"); + ValueType type2 = ValueType::from_spec("tensor<float>(a[10],b[10])"); + ValueType type3 = ValueType::from_spec("tensor<float>(a[5],b[5])"); + TensorInfo info = TensorInfo{"info", {DZ("dim"),DZ("dim")}, TensorInfo::ElementType::FLOAT}; + EXPECT_FALSE(planner.bind_input_type(type1, info)); // binds 'dim' to 5, then fails (5 != 10) + EXPECT_FALSE(planner.bind_input_type(type2, info)); + EXPECT_TRUE(planner.bind_input_type(type3, info)); +} + +TEST(OnnxTest, simple_onnx_model_can_be_inspected) { - OnnxWrapper wrapper(simple_model, OnnxWrapper::Optimize::DISABLE); - dump_info("inputs", wrapper.inputs()); - dump_info("outputs", wrapper.outputs()); - ASSERT_EQ(wrapper.inputs().size(), 3); - ASSERT_EQ(wrapper.outputs().size(), 1); + Onnx model(simple_model, Onnx::Optimize::DISABLE); + dump_info("inputs", model.inputs()); + dump_info("outputs", model.outputs()); + ASSERT_EQ(model.inputs().size(), 3); + ASSERT_EQ(model.outputs().size(), 1); //------------------------------------------------------------------------- - EXPECT_EQ(wrapper.inputs()[0].name, "query_tensor"); - EXPECT_EQ(wrapper.inputs()[0].type_as_string(), "float[1][4]"); + EXPECT_EQ(model.inputs()[0].name, "query_tensor"); + EXPECT_EQ(model.inputs()[0].type_as_string(), "float[1][4]"); //------------------------------------------------------------------------- - EXPECT_EQ(wrapper.inputs()[1].name, "attribute_tensor"); - EXPECT_EQ(wrapper.inputs()[1].type_as_string(), "float[4][1]"); + EXPECT_EQ(model.inputs()[1].name, "attribute_tensor"); + EXPECT_EQ(model.inputs()[1].type_as_string(), "float[4][1]"); //------------------------------------------------------------------------- - EXPECT_EQ(wrapper.inputs()[2].name, "bias_tensor"); - EXPECT_EQ(wrapper.inputs()[2].type_as_string(), "float[1][1]"); + EXPECT_EQ(model.inputs()[2].name, "bias_tensor"); + EXPECT_EQ(model.inputs()[2].type_as_string(), "float[1][1]"); //------------------------------------------------------------------------- - EXPECT_EQ(wrapper.outputs()[0].name, "output"); - EXPECT_EQ(wrapper.outputs()[0].type_as_string(), "float[1][1]"); + EXPECT_EQ(model.outputs()[0].name, "output"); + EXPECT_EQ(model.outputs()[0].type_as_string(), "float[1][1]"); } -TEST(OnnxWrapperTest, onnx_model_can_be_evaluated) +TEST(OnnxTest, dynamic_onnx_model_can_be_inspected) { - OnnxWrapper wrapper(simple_model, OnnxWrapper::Optimize::ENABLE); + Onnx model(dynamic_model, Onnx::Optimize::DISABLE); + dump_info("inputs", model.inputs()); + dump_info("outputs", model.outputs()); + ASSERT_EQ(model.inputs().size(), 3); + ASSERT_EQ(model.outputs().size(), 1); + //------------------------------------------------------------------------- + EXPECT_EQ(model.inputs()[0].name, "query_tensor"); + EXPECT_EQ(model.inputs()[0].type_as_string(), "float[batch][4]"); + //------------------------------------------------------------------------- + EXPECT_EQ(model.inputs()[1].name, "attribute_tensor"); + EXPECT_EQ(model.inputs()[1].type_as_string(), "float[4][1]"); + //------------------------------------------------------------------------- + EXPECT_EQ(model.inputs()[2].name, "bias_tensor"); + EXPECT_EQ(model.inputs()[2].type_as_string(), "float[batch][]"); + //------------------------------------------------------------------------- + EXPECT_EQ(model.outputs()[0].name, "output"); + EXPECT_EQ(model.outputs()[0].type_as_string(), "float[batch][1]"); +} + +TEST(OnnxTest, simple_onnx_model_can_be_evaluated) +{ + Onnx model(simple_model, Onnx::Optimize::ENABLE); + Onnx::WirePlanner planner; ValueType query_type = ValueType::from_spec("tensor<float>(a[1],b[4])"); std::vector<float> query_values({1.0, 2.0, 3.0, 4.0}); DenseTensorView query(query_type, TypedCells(query_values)); - EXPECT_TRUE(wrapper.inputs()[0].is_compatible(query_type)); - EXPECT_FALSE(wrapper.inputs()[1].is_compatible(query_type)); - EXPECT_FALSE(wrapper.inputs()[2].is_compatible(query_type)); + EXPECT_TRUE(planner.bind_input_type(query_type, model.inputs()[0])); ValueType attribute_type = ValueType::from_spec("tensor<float>(a[4],b[1])"); std::vector<float> attribute_values({5.0, 6.0, 7.0, 8.0}); DenseTensorView attribute(attribute_type, TypedCells(attribute_values)); - EXPECT_FALSE(wrapper.inputs()[0].is_compatible(attribute_type)); - EXPECT_TRUE(wrapper.inputs()[1].is_compatible(attribute_type)); - EXPECT_FALSE(wrapper.inputs()[2].is_compatible(attribute_type)); + EXPECT_TRUE(planner.bind_input_type(attribute_type, model.inputs()[1])); ValueType bias_type = ValueType::from_spec("tensor<float>(a[1],b[1])"); std::vector<float> bias_values({9.0}); DenseTensorView bias(bias_type, TypedCells(bias_values)); - EXPECT_FALSE(wrapper.inputs()[0].is_compatible(bias_type)); - EXPECT_FALSE(wrapper.inputs()[1].is_compatible(bias_type)); - EXPECT_TRUE(wrapper.inputs()[2].is_compatible(bias_type)); - - MutableDenseTensorView output(wrapper.outputs()[0].make_compatible_type()); - EXPECT_EQ(output.fast_type().to_spec(), "tensor<float>(d0[1],d1[1])"); - - OnnxWrapper::Params params; - params.bind(0, query); - params.bind(1, attribute); - params.bind(2, bias); - auto result = wrapper.eval(params); - - EXPECT_EQ(result.num_values(), 1); - result.get(0, output); - auto cells = output.cellsRef(); + EXPECT_TRUE(planner.bind_input_type(bias_type, model.inputs()[2])); + + EXPECT_EQ(planner.make_output_type(model.outputs()[0]).to_spec(), + "tensor<float>(d0[1],d1[1])"); + + Onnx::WireInfo wire_info = planner.get_wire_info(model); + Onnx::EvalContext ctx(model, wire_info); + + const Value &output = ctx.get_result(0); + EXPECT_EQ(output.type().to_spec(), "tensor<float>(d0[1],d1[1])"); + //------------------------------------------------------------------------- + ctx.bind_param(0, query); + ctx.bind_param(1, attribute); + ctx.bind_param(2, bias); + ctx.eval(); + auto cells = static_cast<const DenseTensorView&>(output).cellsRef(); EXPECT_EQ(cells.type, ValueType::CellType::FLOAT); EXPECT_EQ(cells.size, 1); EXPECT_EQ(cells.get(0), 79.0); + //------------------------------------------------------------------------- + std::vector<float> new_bias_values({10.0}); + DenseTensorView new_bias(bias_type, TypedCells(new_bias_values)); + ctx.bind_param(2, new_bias); + ctx.eval(); + EXPECT_EQ(static_cast<const DenseTensorView&>(output).cellsRef().get(0), 80.0); + //------------------------------------------------------------------------- +} + +TEST(OnnxTest, dynamic_onnx_model_can_be_evaluated) +{ + Onnx model(dynamic_model, Onnx::Optimize::ENABLE); + Onnx::WirePlanner planner; + + ValueType query_type = ValueType::from_spec("tensor<float>(a[1],b[4])"); + std::vector<float> query_values({1.0, 2.0, 3.0, 4.0}); + DenseTensorView query(query_type, TypedCells(query_values)); + EXPECT_TRUE(planner.bind_input_type(query_type, model.inputs()[0])); + + ValueType attribute_type = ValueType::from_spec("tensor<float>(a[4],b[1])"); + std::vector<float> attribute_values({5.0, 6.0, 7.0, 8.0}); + DenseTensorView attribute(attribute_type, TypedCells(attribute_values)); + EXPECT_TRUE(planner.bind_input_type(attribute_type, model.inputs()[1])); + + ValueType bias_type = ValueType::from_spec("tensor<float>(a[1],b[2])"); + std::vector<float> bias_values({4.0, 5.0}); + DenseTensorView bias(bias_type, TypedCells(bias_values)); + EXPECT_TRUE(planner.bind_input_type(bias_type, model.inputs()[2])); + + EXPECT_EQ(planner.make_output_type(model.outputs()[0]).to_spec(), + "tensor<float>(d0[1],d1[1])"); + + Onnx::WireInfo wire_info = planner.get_wire_info(model); + Onnx::EvalContext ctx(model, wire_info); + + const Value &output = ctx.get_result(0); + EXPECT_EQ(output.type().to_spec(), "tensor<float>(d0[1],d1[1])"); + //------------------------------------------------------------------------- + ctx.bind_param(0, query); + ctx.bind_param(1, attribute); + ctx.bind_param(2, bias); + ctx.eval(); + auto cells = static_cast<const DenseTensorView&>(output).cellsRef(); + EXPECT_EQ(cells.type, ValueType::CellType::FLOAT); + EXPECT_EQ(cells.size, 1); + EXPECT_EQ(cells.get(0), 79.0); + //------------------------------------------------------------------------- + std::vector<float> new_bias_values({5.0,6.0}); + DenseTensorView new_bias(bias_type, TypedCells(new_bias_values)); + ctx.bind_param(2, new_bias); + ctx.eval(); + EXPECT_EQ(static_cast<const DenseTensorView&>(output).cellsRef().get(0), 81.0); + //------------------------------------------------------------------------- } GTEST_MAIN_RUN_ALL_TESTS() |