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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.tensor;
import com.yahoo.tensor.evaluation.MapEvaluationContext;
import com.yahoo.tensor.evaluation.Name;
import com.yahoo.tensor.evaluation.VariableTensor;
import com.yahoo.tensor.functions.ConstantTensor;
import com.yahoo.tensor.functions.Join;
import com.yahoo.tensor.functions.Reduce;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.Collections;
import java.util.List;
import java.util.Random;
/**
* Microbenchmark of a "dot product" of two mapped rank 2 tensors
*
* @author bratseth
*/
public class MatrixDotProductBenchmark {
private final static Random random = new Random();
public double benchmark(int iterations, List<Tensor> modelMatrixes, TensorType.Dimension.Type dimensionType) {
Tensor queryMatrix = matrix(1, 20, dimensionType).get(0);
dotProduct(queryMatrix, modelMatrixes, Math.max(iterations/10, 10)); // warmup
System.gc();
long startTime = System.currentTimeMillis();
dotProduct(queryMatrix, modelMatrixes, iterations);
long totalTime = System.currentTimeMillis() - startTime;
return (double)totalTime / (double)iterations;
}
private double dotProduct(Tensor tensor, List<Tensor> tensors, int iterations) {
double result = 0;
for (int i = 0 ; i < iterations; i++)
result = dotProduct(tensor, tensors);
return result;
}
private double dotProduct(Tensor tensor, List<Tensor> tensors) {
double largest = Double.MIN_VALUE;
TensorFunction<Name> dotProductFunction = new Reduce<>(new Join<>(new ConstantTensor<>(tensor),
new VariableTensor<>("argument"), (a, b) -> a * b),
Reduce.Aggregator.sum).toPrimitive();
MapEvaluationContext<Name> context = new MapEvaluationContext<>();
for (Tensor tensorElement : tensors) { // tensors.size() = 1 for larger tensor
context.put("argument", tensorElement);
double dotProduct = dotProductFunction.evaluate(context).asDouble();
if (dotProduct > largest) {
largest = dotProduct;
}
}
return largest;
}
private static List<Tensor> matrix(int dimension1Size, int dimension2Size, TensorType.Dimension.Type dimensionType) {
TensorType.Builder typeBuilder = new TensorType.Builder();
addDimension(typeBuilder, "i", dimensionType, dimension1Size);
addDimension(typeBuilder, "j", dimensionType, dimension2Size);
Tensor.Builder builder = Tensor.Builder.of(typeBuilder.build());
for (int i = 0; i < dimension1Size; i++) {
for (int j = 0; j < dimension2Size; j++) {
builder.cell()
.label("i", String.valueOf("label" + i))
.label("j", String.valueOf("label" + j))
.value(random.nextDouble());
}
}
return Collections.singletonList(builder.build());
}
private static void addDimension(TensorType.Builder builder, String name, TensorType.Dimension.Type type, int size) {
switch (type) {
case mapped: builder.mapped(name); break;
case indexedUnbound: builder.indexed(name); break;
case indexedBound: builder.indexed(name, size); break;
default: throw new IllegalArgumentException("Dimension type " + type + " not supported");
}
}
public static void main(String[] args) {
double time = new MatrixDotProductBenchmark().benchmark(10000, matrix(10, 55, TensorType.Dimension.Type.mapped), TensorType.Dimension.Type.mapped);
System.out.printf("Matrixes, 10*55 size matrixes. Time per sum(join): %1$8.3f ms\n", time);
}
}
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