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package ai.vespa.llm.generation;
import ai.vespa.modelintegration.evaluator.OnnxEvaluator;
import ai.vespa.modelintegration.evaluator.OnnxEvaluatorOptions;
import ai.vespa.modelintegration.evaluator.OnnxRuntime;
import com.yahoo.component.AbstractComponent;
import com.yahoo.component.annotation.Inject;
import com.yahoo.language.process.Embedder;
import com.yahoo.language.sentencepiece.SentencePieceEmbedder;
import com.yahoo.llm.GeneratorConfig;
import com.yahoo.tensor.DimensionSizes;
import com.yahoo.tensor.IndexedTensor;
import com.yahoo.tensor.PartialAddress;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.api.annotations.Beta;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
/**
* A text generator based on language models (LLMs). By configuring a
* sentencepience tokenizer and models for encoding and decoding, this
* component generates text based on the given prompt.
*
* See llm.generator.def for configurable parameters.
*
* @author lesters
*/
@Beta
public class Generator extends AbstractComponent {
private final static int TOKEN_EOS = 1; // end of sequence
private final static String BATCH_DIMENSION = "d0";
private final static String SEQUENCE_DIMENSION = "d1";
private final int tokenizerMaxTokens;
private final String encoderInputIdsName;
private final String encoderAttentionMaskName;
private final String encoderOutputName;
private final String decoderInputIdsName;
private final String decoderAttentionMaskName;
private final String decoderEncoderHiddenStateName;
private final String decoderOutputName;
private final SentencePieceEmbedder tokenizer;
private final OnnxEvaluator encoder;
private final OnnxEvaluator decoder;
@Inject
public Generator(OnnxRuntime onnx, GeneratorConfig config) {
// Set up tokenizer
tokenizer = new SentencePieceEmbedder.Builder(config.tokenizerModel().toString()).build();
tokenizerMaxTokens = config.tokenizerMaxTokens();
// Set up encoder
encoderInputIdsName = config.encoderModelInputIdsName();
encoderAttentionMaskName = config.encoderModelAttentionMaskName();
encoderOutputName = config.encoderModelOutputName();
OnnxEvaluatorOptions encoderOptions = new OnnxEvaluatorOptions();
encoderOptions.setExecutionMode(config.encoderOnnxExecutionMode().toString());
encoderOptions.setThreads(config.encoderOnnxInterOpThreads(), config.encoderOnnxIntraOpThreads());
encoder = onnx.evaluatorOf(config.encoderModel().toString(), encoderOptions);
// Set up decoder
decoderInputIdsName = config.decoderModelInputIdsName();
decoderAttentionMaskName = config.decoderModelAttentionMaskName();
decoderEncoderHiddenStateName = config.decoderModelEncoderHiddenStateName();
decoderOutputName = config.decoderModelOutputName();
OnnxEvaluatorOptions decoderOptions = new OnnxEvaluatorOptions();
decoderOptions.setExecutionMode(config.decoderOnnxExecutionMode().toString());
decoderOptions.setThreads(config.decoderOnnxInterOpThreads(), config.decoderOnnxIntraOpThreads());
decoder = onnx.evaluatorOf(config.decoderModel().toString(), decoderOptions);
validateModels();
}
/**
* Generates text by evaluating an encoder model to encode the prompt, and
* repeatedly evaluating a decoding model to generate tokens until some
* stopping criteria has been met.
*
* @param prompt the prompt to generate text from
* @param options options for text generation
* @return a text generated from the prompt
*/
public String generate(String prompt, GeneratorOptions options) {
return switch (options.getSearchMethod()) {
case GREEDY -> generateGreedy(prompt, options);
default -> generateNotImplemented(options);
};
}
public String generate(String prompt) {
return generate(prompt, new GeneratorOptions());
}
@Override public void deconstruct() { encoder.close(); decoder.close(); }
private String generateNotImplemented(GeneratorOptions options) {
throw new UnsupportedOperationException("Search method '" + options.getSearchMethod() + "' is currently not implemented");
}
private String generateGreedy(String prompt, GeneratorOptions options) {
var generatedTokens = new ArrayList<Integer>();
generatedTokens.add(0); // Or target tokens
// Tokenize
var inputTokens = tokenize(prompt); // Or source tokens
// Evaluate encoder
var encoderInput = createTensorRepresentation(inputTokens, SEQUENCE_DIMENSION);
var encoderMask = createAttentionMask(encoderInput).expand(BATCH_DIMENSION);
var encoderOutput = evaluateEncoder(encoderInput.expand(BATCH_DIMENSION), encoderMask);
// Greedy search just grabs the next most probable token
while (generatedTokens.size() < options.getMaxLength()) { // Todo: add stopping criteria
var decoderInput = createTensorRepresentation(generatedTokens, SEQUENCE_DIMENSION).expand(BATCH_DIMENSION);
var logits = evaluateDecoder(decoderInput, encoderMask, encoderOutput);
var nextToken = findMostProbableToken(logits, generatedTokens.size()-1, BATCH_DIMENSION, SEQUENCE_DIMENSION);
generatedTokens.add(nextToken);
}
return detokenize(generatedTokens);
}
private Tensor evaluateEncoder(Tensor input, Tensor mask) {
var encoderInputs = Map.of(encoderInputIdsName, input,
encoderAttentionMaskName, mask);
return encoder.evaluate(encoderInputs, encoderOutputName);
}
private IndexedTensor evaluateDecoder(Tensor input, Tensor encoderMask, Tensor encoderOutput) {
var inputs = Map.of(decoderInputIdsName, input,
decoderAttentionMaskName, encoderMask, // yes, encoder's attention mask
decoderEncoderHiddenStateName, encoderOutput);
var output = decoder.evaluate(inputs, decoderOutputName);
if ( ! (output instanceof IndexedTensor indexedTensor)) {
throw new IllegalArgumentException("Output of decoder model is not an 'IndexedTensor'");
}
return indexedTensor;
}
/**
* Given a tensor 'logits' with 3 dimensions: batch, sequence, and vocabulary
* find the value in the vocabulary dimension with highest score for the given
* token in the sequence
*/
private static int findMostProbableToken(IndexedTensor logits, int seqIndex, String batchDim, String seqDim) {
if (logits.type().rank() != 3) {
throw new IllegalArgumentException("Expected a tensor with rank 3: batch, sequence, and vocabulary size. " +
"Got: " + logits.type());
}
var iterator = logits.cellIterator(new PartialAddress.Builder(2).
add(batchDim, 0).
add(seqDim, seqIndex).build(),
DimensionSizes.of(logits.type()));
var maxVal = iterator.next().getValue();
int maxIndex = 0;
for (int i = 1; iterator.hasNext(); ++i) {
var val = iterator.next().getValue();
if (val >= maxVal && i != TOKEN_EOS) {
maxVal = val;
maxIndex = i;
}
}
return maxIndex;
}
private List<Integer> tokenize(String text) {
var tokens = tokenizer.embed(text, new Embedder.Context("tokenizer"));
tokens = tokens.size() >= tokenizerMaxTokens ? tokens.subList(0,tokenizerMaxTokens-1): tokens;
tokens.add(TOKEN_EOS);
return tokens;
}
private String detokenize(List<Integer> tokens) {
return tokenizer.decode(tokens, new Embedder.Context("tokenizer"), true);
}
private static Tensor createTensorRepresentation(List<Integer> tokens, String dimension) {
var size = tokens.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(tokens.get(i), i);
}
return builder.build();
}
private static Tensor createAttentionMask(Tensor d) {
return d.map((x) -> x > 0 ? 1:0);
}
private void validateModels() {
Map<String, TensorType> inputs = encoder.getInputInfo();
validateName(inputs, encoderInputIdsName, "input");
validateName(inputs, encoderAttentionMaskName, "input");
Map<String, TensorType> outputs = encoder.getOutputInfo();
validateName(outputs, encoderOutputName, "output");
inputs = decoder.getInputInfo();
validateName(inputs, decoderInputIdsName, "input");
validateName(inputs, decoderAttentionMaskName, "input");
validateName(inputs, decoderEncoderHiddenStateName, "input");
outputs = decoder.getOutputInfo();
validateName(outputs, decoderOutputName, "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()));
}
}
}
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