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// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/eval/tensor/dense/onnx_wrapper.h>
#include <vespa/eval/tensor/dense/dense_tensor_view.h>
#include <vespa/eval/tensor/dense/mutable_dense_tensor_view.h>
#include <vespa/vespalib/util/stringfmt.h>
#include <vespa/vespalib/gtest/gtest.h>
using namespace vespalib::eval;
using namespace vespalib::tensor;
using vespalib::make_string_short::fmt;
using TensorInfo = Onnx::TensorInfo;
using ElementType = Onnx::ElementType;
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 simple_model = source_dir + "/simple.onnx";
std::string dynamic_model = source_dir + "/dynamic.onnx";
std::string int_types_model = source_dir + "/int_types.onnx";
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(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)}, 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")}, 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()}, 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()}, ElementType::FLOAT};
TensorInfo info2 = TensorInfo{"info", {DZ("dim")}, ElementType::FLOAT};
TensorInfo info3 = TensorInfo{"info", {DZ("dim"),DZ()}, 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")}, 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)
{
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(model.inputs()[0].name, "query_tensor");
EXPECT_EQ(model.inputs()[0].type_as_string(), "float[1][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[1][1]");
//-------------------------------------------------------------------------
EXPECT_EQ(model.outputs()[0].name, "output");
EXPECT_EQ(model.outputs()[0].type_as_string(), "float[1][1]");
}
TEST(OnnxTest, dynamic_onnx_model_can_be_inspected)
{
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(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[1])");
std::vector<float> bias_values({9.0});
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({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);
//-------------------------------------------------------------------------
}
TEST(OnnxTest, int_types_onnx_model_can_be_evaluated)
{
Onnx model(int_types_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<double>(a[4],b[1])");
std::vector<double> 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<double>(a[1],b[1])");
std::vector<double> bias_values({9.0});
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]),
ValueType::from_spec("tensor<double>(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(), ValueType::from_spec("tensor<double>(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::DOUBLE);
EXPECT_EQ(cells.size, 1);
EXPECT_EQ(cells.get(0), 79.0);
//-------------------------------------------------------------------------
std::vector<double> 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);
//-------------------------------------------------------------------------
}
GTEST_MAIN_RUN_ALL_TESTS()
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