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package com.yahoo.tensor;
import com.yahoo.tensor.functions.Reduce;
import java.util.*;
/**
* Microbenchmark of tensor operations.
*
* @author bratseth
*/
public class TensorFunctionBenchmark {
private final static Random random = new Random();
public double benchmark(int iterations, List<Tensor> modelVectors) {
Tensor queryVector = generateVectors(1, 300).get(0);
dotProduct(queryVector, modelVectors, 10); // warmup
long startTime = System.currentTimeMillis();
dotProduct(queryVector, modelVectors, iterations);
long totalTime = System.currentTimeMillis() - startTime;
return totalTime / 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;
for (Tensor tensorElement : tensors) { // tensors.size() = 1 for larger tensor
Tensor result = tensor.join(tensorElement, (a, b) -> a * b).reduce(Reduce.Aggregator.sum, "x");
double dotProduct = result.reduce(Reduce.Aggregator.max).asDouble(); // for larger tensor
if (dotProduct > largest) {
largest = dotProduct;
}
}
System.out.println(largest);
return largest;
}
private static List<Tensor> generateVectors(int vectorCount, int vectorSize) {
List<Tensor> tensors = new ArrayList<>();
TensorType type = new TensorType.Builder().mapped("x").build();
for (int i = 0; i < vectorCount; i++) {
MapTensorBuilder builder = new MapTensorBuilder(type);
for (int j = 0; j < vectorSize; j++) {
builder.cell().label("x", String.valueOf(j)).value(random.nextDouble());
}
tensors.add(builder.build());
}
return tensors;
}
private static List<Tensor> generateVectorsInOneTensor(int vectorCount, int vectorSize) {
List<Tensor> tensors = new ArrayList<>();
TensorType type = new TensorType.Builder().mapped("i").mapped("x").build();
MapTensorBuilder builder = new MapTensorBuilder(type);
for (int i = 0; i < vectorCount; i++) {
for (int j = 0; j < vectorSize; j++) {
builder.cell()
.label("i", String.valueOf(i))
.label("x", String.valueOf(j))
.value(random.nextDouble());
}
}
tensors.add(builder.build());
return tensors; // only one tensor in the list.
}
public static void main(String[] args) {
// Was: 150 ms
// After adding type: 300 ms
// After sorting dimensions: 100 ms
// After special-casing single space: 4 ms
double timeperJoin = new TensorFunctionBenchmark().benchmark(100, generateVectors(100, 300));
// This benchmark should be as fast as fast as the previous. Currently it is not by a factor of 600
double timePerJoinOneTensor = new TensorFunctionBenchmark().benchmark(20, generateVectorsInOneTensor(100, 300));
System.out.println("Time per join: " + timeperJoin + " ms");
System.out.println("Time per join, one tensor: " + timePerJoinOneTensor + " ms");
}
}
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