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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.modelintegration.evaluator;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import org.junit.Test;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.Map;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import static org.junit.Assume.assumeTrue;
/**
* @author lesters
*/
public class OnnxEvaluatorTest {
@Test
public void testSimpleModel() {
assumeTrue(OnnxRuntime.isRuntimeAvailable());
var runtime = new OnnxRuntime();
OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/simple/simple.onnx");
// Input types
Map<String, TensorType> inputTypes = evaluator.getInputInfo();
assertEquals(inputTypes.get("query_tensor"), TensorType.fromSpec("tensor<float>(d0[1],d1[4])"));
assertEquals(inputTypes.get("attribute_tensor"), TensorType.fromSpec("tensor<float>(d0[4],d1[1])"));
assertEquals(inputTypes.get("bias_tensor"), TensorType.fromSpec("tensor<float>(d0[1],d1[1])"));
// Output types
Map<String, TensorType> outputTypes = evaluator.getOutputInfo();
assertEquals(outputTypes.get("output"), TensorType.fromSpec("tensor<float>(d0[1],d1[1])"));
// Evaluation
Map<String, Tensor> inputs = new HashMap<>();
inputs.put("query_tensor", Tensor.from("tensor(d0[1],d1[4]):[0.1, 0.2, 0.3, 0.4]"));
inputs.put("attribute_tensor", Tensor.from("tensor(d0[4],d1[1]):[0.1, 0.2, 0.3, 0.4]"));
inputs.put("bias_tensor", Tensor.from("tensor(d0[1],d1[1]):[1.0]"));
assertEquals(evaluator.evaluate(inputs).get("output"), Tensor.from("tensor(d0[1],d1[1]):[1.3]"));
assertEquals(evaluator.evaluate(inputs, "output"), Tensor.from("tensor(d0[1],d1[1]):[1.3]"));
}
@Test
public void testBatchDimension() {
assumeTrue(OnnxRuntime.isRuntimeAvailable());
var runtime = new OnnxRuntime();
OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/pytorch/one_layer.onnx");
// Input types
Map<String, TensorType> inputTypes = evaluator.getInputInfo();
assertEquals(inputTypes.get("input"), TensorType.fromSpec("tensor<float>(d0[],d1[3])"));
// Output types
Map<String, TensorType> outputTypes = evaluator.getOutputInfo();
assertEquals(outputTypes.get("output"), TensorType.fromSpec("tensor<float>(d0[],d1[1])"));
// Evaluation
Map<String, Tensor> inputs = new HashMap<>();
inputs.put("input", Tensor.from("tensor<float>(d0[2],d1[3]):[[0.1, 0.2, 0.3],[0.4,0.5,0.6]]"));
assertEquals(evaluator.evaluate(inputs, "output"), Tensor.from("tensor<float>(d0[2],d1[1]):[0.6393113,0.67574286]"));
}
@Test
public void testMatMul() {
assumeTrue(OnnxRuntime.isRuntimeAvailable());
var runtime = new OnnxRuntime();
String expected = "tensor<float>(d0[2],d1[4]):[38,44,50,56,83,98,113,128]";
String input1 = "tensor<float>(d0[2],d1[3]):[1,2,3,4,5,6]";
String input2 = "tensor<float>(d0[3],d1[4]):[1,2,3,4,5,6,7,8,9,10,11,12]";
assertEvaluate(runtime, "simple/matmul.onnx", expected, input1, input2);
}
@Test
public void testTypes() {
assumeTrue(OnnxRuntime.isRuntimeAvailable());
var runtime = new OnnxRuntime();
assertEvaluate(runtime, "add_double.onnx", "tensor(d0[1]):[3]", "tensor(d0[1]):[1]", "tensor(d0[1]):[2]");
assertEvaluate(runtime, "add_float.onnx", "tensor<float>(d0[1]):[3]", "tensor<float>(d0[1]):[1]", "tensor<float>(d0[1]):[2]");
assertEvaluate(runtime, "add_int64.onnx", "tensor<double>(d0[1]):[3]", "tensor<double>(d0[1]):[1]", "tensor<double>(d0[1]):[2]");
assertEvaluate(runtime, "cast_int8_float.onnx", "tensor<float>(d0[1]):[-128]", "tensor<int8>(d0[1]):[128]");
assertEvaluate(runtime, "cast_float_int8.onnx", "tensor<int8>(d0[1]):[-1]", "tensor<float>(d0[1]):[255]");
// ONNX Runtime 1.8.0 does not support much of bfloat16 yet
// assertEvaluate("cast_bfloat16_float.onnx", "tensor<float>(d0[1]):[1]", "tensor<bfloat16>(d0[1]):[1]");
}
@Test
public void testNotIdentifiers() {
assumeTrue(OnnxRuntime.isRuntimeAvailable());
var runtime = new OnnxRuntime();
OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/badnames.onnx");
var inputInfo = evaluator.getInputInfo();
var outputInfo = evaluator.getOutputInfo();
for (var entry : inputInfo.entrySet()) {
System.out.println("wants input: " + entry.getKey() + " with type " + entry.getValue());
}
for (var entry : outputInfo.entrySet()) {
System.out.println("will produce output: " + entry.getKey() + " with type " + entry.getValue());
}
assertEquals(3, inputInfo.size());
assertTrue(inputInfo.containsKey("first_input"));
assertTrue(inputInfo.containsKey("second_input_0"));
assertTrue(inputInfo.containsKey("third_input"));
assertEquals(3, outputInfo.size());
assertTrue(outputInfo.containsKey("path_to_output_0"));
assertTrue(outputInfo.containsKey("path_to_output_1"));
assertTrue(outputInfo.containsKey("path_to_output_2"));
Map<String, Tensor> inputs = new HashMap<>();
inputs.put("first_input", Tensor.from("tensor(d0[2]):[2,3]"));
inputs.put("second_input_0", Tensor.from("tensor(d0[2]):[4,5]"));
inputs.put("third_input", Tensor.from("tensor(d0[2]):[6,7]"));
Tensor result;
result = evaluator.evaluate(inputs, "path_to_output_0");
System.out.println("got result: " + result);
assertTrue(result != null);
result = evaluator.evaluate(inputs, "path_to_output_1");
System.out.println("got result: " + result);
assertTrue(result != null);
result = evaluator.evaluate(inputs, "path_to_output_2");
System.out.println("got result: " + result);
assertTrue(result != null);
var allResults = evaluator.evaluate(inputs);
assertTrue(allResults != null);
for (var entry : allResults.entrySet()) {
System.out.println("produced output: " + entry.getKey() + " with type " + entry.getValue());
}
assertEquals(3, allResults.size());
assertTrue(allResults.containsKey("path_to_output_0"));
assertTrue(allResults.containsKey("path_to_output_1"));
assertTrue(allResults.containsKey("path_to_output_2"));
// we can also get output by onnx-internal name
result = evaluator.evaluate(inputs, "path/to/output:0");
System.out.println("got result: " + result);
assertTrue(result != null);
// we can also send input by onnx-internal name
inputs.remove("second_input_0");
inputs.put("second/input:0", Tensor.from("tensor(d0[2]):[8,9]"));
allResults = evaluator.evaluate(inputs);
assertTrue(allResults != null);
for (var entry : allResults.entrySet()) {
System.out.println("produced output: " + entry.getKey() + " with type " + entry.getValue());
}
assertEquals(3, allResults.size());
}
@Test
public void testLoadModelFromBytes() throws IOException {
assumeTrue(OnnxRuntime.isRuntimeAvailable());
var runtime = new OnnxRuntime();
var model = Files.readAllBytes(Paths.get("src/test/models/onnx/simple/simple.onnx"));
var evaluator = runtime.evaluatorOf(model);
assertEquals(3, evaluator.getInputs().size());
assertEquals(1, evaluator.getOutputs().size());
evaluator.close();
}
private void assertEvaluate(OnnxRuntime runtime, String model, String output, String... input) {
OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/" + model);
Map<String, Tensor> inputs = new HashMap<>();
for (int i = 0; i < input.length; ++i) {
inputs.put("input" + (i+1), Tensor.from(input[i]));
}
Tensor expected = Tensor.from(output);
Tensor result = evaluator.evaluate(inputs, "output");
assertEquals(expected, result);
assertEquals(expected.type().valueType(), result.type().valueType());
}
}
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