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package ai.vespa.embedding.huggingface;
import ai.vespa.modelintegration.evaluator.OnnxEvaluator;
import ai.vespa.modelintegration.evaluator.OnnxEvaluatorOptions;
import ai.vespa.modelintegration.evaluator.OnnxRuntime;
import com.yahoo.api.annotations.Beta;
import com.yahoo.component.AbstractComponent;
import com.yahoo.component.annotation.Inject;
import com.yahoo.embedding.huggingface.HuggingFaceEmbedderConfig;
import com.yahoo.language.huggingface.HuggingFaceTokenizer;
import com.yahoo.language.process.Embedder;
import com.yahoo.tensor.IndexedTensor;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorAddress;
import com.yahoo.tensor.TensorType;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.nio.file.Paths;
import java.util.List;
import java.util.Map;
@Beta
public class HuggingFaceEmbedder extends AbstractComponent implements Embedder {
private static final Logger LOG = LoggerFactory.getLogger(HuggingFaceEmbedder.class.getName());
private final String inputIdsName;
private final String attentionMaskName;
private final String outputName;
private final int maxTokens;
private final boolean normalize;
private final HuggingFaceTokenizer tokenizer;
private final OnnxEvaluator evaluator;
@Inject
public HuggingFaceEmbedder(OnnxRuntime onnx, HuggingFaceEmbedderConfig config) {
maxTokens = config.transformerMaxTokens();
inputIdsName = config.transformerInputIds();
attentionMaskName = config.transformerAttentionMask();
outputName = config.transformerOutput();
normalize = config.normalize();
tokenizer = new HuggingFaceTokenizer.Builder()
.addSpecialTokens(true)
.addDefaultModel(Paths.get(config.tokenizerPath().toString()))
.build();
var onnxOpts = new OnnxEvaluatorOptions();
if (config.transformerGpuDevice() >= 0)
onnxOpts.setGpuDevice(config.transformerGpuDevice());
onnxOpts.setExecutionMode(config.transformerExecutionMode().toString());
onnxOpts.setThreads(config.transformerInterOpThreads(), config.transformerIntraOpThreads());
evaluator = onnx.evaluatorOf(config.transformerModel().toString(), onnxOpts);
validateModel();
}
public void validateModel() {
Map<String, TensorType> inputs = evaluator.getInputInfo();
validateName(inputs, inputIdsName, "input");
validateName(inputs, attentionMaskName, "input");
Map<String, TensorType> outputs = evaluator.getOutputInfo();
validateName(outputs, outputName, "output");
}
private void validateName(Map<String, TensorType> types, String name, String type) {
if ( ! types.containsKey(name)) {
throw new IllegalArgumentException("Model does not contain required " + type + ": '" + name + "'. " +
"Model contains: " + String.join(",", types.keySet()));
}
}
@Override
public List<Integer> embed(String s, Context context) {
var tokenIds = tokenizer.embed(s, context);
int tokensSize = tokenIds.size();
if (tokensSize > maxTokens) {
Integer lastElement = tokenIds.get(tokensSize - 1);
tokenIds = tokenIds.subList(0, maxTokens - 1);
tokenIds.add(lastElement);
}
return tokenIds;
}
@Override
public void deconstruct() {
evaluator.close();
tokenizer.close();
}
@Override
public Tensor embed(String s, Context context, TensorType tensorType) {
List<Integer> tokenIds = embed(s, context);
return embedTokens(tokenIds, tensorType);
}
Tensor embedTokens(List<Integer> tokenIds, TensorType tensorType) {
Tensor inputSequence = createTensorRepresentation(tokenIds, "d1");
Tensor attentionMask = createAttentionMask(inputSequence);
Map<String, Tensor> inputs = Map.of(
inputIdsName, inputSequence.expand("d0"),
attentionMaskName, attentionMask.expand("d0")
);
Map<String, Tensor> outputs = evaluator.evaluate(inputs);
Tensor tokenEmbeddings = outputs.get(outputName);
Tensor.Builder builder = Tensor.Builder.of(tensorType);
// Mean pooling implementation
Tensor summedEmbeddings = tokenEmbeddings.sum("d1");
Tensor summedAttentionMask = attentionMask.expand("d0").sum("d1");
Tensor averaged = summedEmbeddings.join(summedAttentionMask, (x, y) -> x / y);
for (int i = 0; i < tensorType.dimensions().get(0).size().get(); i++) {
builder.cell(averaged.get(TensorAddress.of(0,i)), i);
}
Tensor result = builder.build();
return normalize ? normalize(result, tensorType) : result;
}
Tensor normalize(Tensor embedding, TensorType tensorType) {
double sumOfSquares = 0.0;
Tensor.Builder builder = Tensor.Builder.of(tensorType);
for (int i = 0; i < tensorType.dimensions().get(0).size().get(); i++) {
double item = embedding.get(TensorAddress.of(i));
sumOfSquares += item * item;
}
double magnitude = Math.sqrt(sumOfSquares);
for (int i = 0; i < tensorType.dimensions().get(0).size().get(); i++) {
double value = embedding.get(TensorAddress.of(i));
builder.cell(value / magnitude, i);
}
return builder.build();
}
private IndexedTensor createTensorRepresentation(List<Integer> input, String dimension) {
int size = input.size();
TensorType type = new TensorType.Builder(TensorType.Value.FLOAT).indexed(dimension, size).build();
IndexedTensor.Builder builder = IndexedTensor.Builder.of(type);
for (int i = 0; i < size; ++i) {
builder.cell(input.get(i), i);
}
return builder.build();
}
private Tensor createAttentionMask(Tensor inputSequence) {
return inputSequence.map((x) -> 1);
}
}
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