[WIP] Support additional output formats for sparse models

common/src/main/java/org/opensearch/ml/common/input/nlp/TextDocsMLInput.java

@@ -27,10 +27,7 @@
  * ML input class which supports a list fo text docs.
  * This class can be used for TEXT_EMBEDDING model.
  */
-@org.opensearch.ml.common.annotation.MLInput(functionNames = {
-    FunctionName.TEXT_EMBEDDING,
-    FunctionName.SPARSE_ENCODING,
-    FunctionName.SPARSE_TOKENIZE })
+@org.opensearch.ml.common.annotation.MLInput(functionNames = { FunctionName.TEXT_EMBEDDING })
 public class TextDocsMLInput extends MLInput {
     public static final String TEXT_DOCS_FIELD = "text_docs";
     public static final String RESULT_FILTER_FIELD = "result_filter";

common/src/main/java/org/opensearch/ml/common/input/parameter/textembedding/SparseEncodingParameters.java

@@ -0,0 +1,95 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.ml.common.input.parameter.textembedding;
+
+import static org.opensearch.core.xcontent.XContentParserUtils.ensureExpectedToken;
+
+import java.io.IOException;
+import java.util.Locale;
+
+import org.opensearch.core.ParseField;
+import org.opensearch.core.common.io.stream.StreamOutput;
+import org.opensearch.core.xcontent.NamedXContentRegistry;
+import org.opensearch.core.xcontent.XContentBuilder;
+import org.opensearch.core.xcontent.XContentParser;
+import org.opensearch.ml.common.FunctionName;
+import org.opensearch.ml.common.annotation.MLAlgoParameter;
+import org.opensearch.ml.common.input.parameter.MLAlgoParams;
+
+import lombok.Builder;
+
+@MLAlgoParameter(algorithms = { FunctionName.SPARSE_ENCODING })
+public class SparseEncodingParameters implements MLAlgoParams {
+
+    public static final String PARSE_FIELD_NAME = FunctionName.SPARSE_ENCODING.name();
+    public static final String SPARSE_ENCODING_FORMAT_FIELD = "sparse_encoding_format";
+
+    @Override
+    public int getVersion() {
+        return 1;
+    }
+
+    @Override
+    public String getWriteableName() {
+        return PARSE_FIELD_NAME;
+    }
+
+    @Override
+    public void writeTo(StreamOutput out) throws IOException {
+        out.writeOptionalString(sparseEncodingType.name());
+    }
+
+    public static final NamedXContentRegistry.Entry XCONTENT_REGISTRY = new NamedXContentRegistry.Entry(
+        MLAlgoParams.class,
+        new ParseField(PARSE_FIELD_NAME),
+        SparseEncodingParameters::parse
+    );
+
+    @Override
+    public XContentBuilder toXContent(XContentBuilder xContentBuilder, Params params) throws IOException {
+        xContentBuilder.startObject();
+        if (sparseEncodingType != null) {
+            xContentBuilder.field(SPARSE_ENCODING_FORMAT_FIELD, sparseEncodingType.name());
+        }
+        xContentBuilder.endObject();
+        return xContentBuilder;
+    }
+
+    public enum SparseEncodingFormat {
+        WORD,
+        INT
+    }
+
+    // The type of the content to be embedded
+    private final SparseEncodingFormat sparseEncodingType;
+
+    @Builder(toBuilder = true)
+    public SparseEncodingParameters(SparseEncodingFormat sparseEncodingType) {
+        this.sparseEncodingType = sparseEncodingType;
+    }
+
+    public SparseEncodingFormat getSparseEncodingType() {
+        return sparseEncodingType;
+    }
+
+    public static MLAlgoParams parse(XContentParser parser) throws IOException {
+        SparseEncodingFormat sparseEncodingType = null;
+
+        ensureExpectedToken(XContentParser.Token.START_OBJECT, parser.currentToken(), parser);
+        while (parser.nextToken() != XContentParser.Token.END_OBJECT) {
+            String fieldName = parser.currentName();
+            parser.nextToken();
+
+            if (fieldName.equals(SPARSE_ENCODING_FORMAT_FIELD)) {
+                String contentType = parser.text();
+                sparseEncodingType = SparseEncodingFormat.valueOf(contentType.toUpperCase(Locale.ROOT));
+            } else {
+                parser.skipChildren();
+            }
+        }
+        return new SparseEncodingParameters(sparseEncodingType);
+    }
+}

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/TextEmbeddingModel.java

@@ -12,6 +12,7 @@
 import org.opensearch.ml.common.input.parameter.MLAlgoParams;
 import org.opensearch.ml.common.input.parameter.textembedding.AsymmetricTextEmbeddingParameters;
 import org.opensearch.ml.common.input.parameter.textembedding.AsymmetricTextEmbeddingParameters.EmbeddingContentType;
+import org.opensearch.ml.common.input.parameter.textembedding.SparseEncodingParameters;
 import org.opensearch.ml.common.model.MLModelConfig;
 import org.opensearch.ml.common.model.TextEmbeddingModelConfig;
 import org.opensearch.ml.common.output.model.ModelResultFilter;
@@ -40,6 +41,10 @@ public ModelTensorOutput predict(String modelId, MLInput mlInput) throws Transla
         for (String doc : textDocsInput.getDocs()) {
             Input input = new Input();
             input.add(doc);
+            if (mlParams instanceof SparseEncodingParameters) {
+                input.add("sparse_encoding_format", ((SparseEncodingParameters) mlParams).getSparseEncodingType().name());
+            }
+
             output = getPredictor().predict(input);
             tensorOutputs.add(parseModelTensorOutput(output, resultFilter));
         }

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/SparseEncodingTranslator.java

@@ -6,34 +6,48 @@
 package org.opensearch.ml.engine.algorithms.sparse_encoding;
 
 import static org.opensearch.ml.common.CommonValue.ML_MAP_RESPONSE_KEY;
+import static org.opensearch.ml.common.input.parameter.textembedding.SparseEncodingParameters.SPARSE_ENCODING_FORMAT_FIELD;
 
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.HashMap;
-import java.util.Iterator;
 import java.util.List;
 import java.util.Map;
 
+import org.opensearch.ml.common.input.parameter.textembedding.SparseEncodingParameters;
 import org.opensearch.ml.common.output.model.ModelTensor;
 import org.opensearch.ml.common.output.model.ModelTensors;
 import org.opensearch.ml.engine.algorithms.SentenceTransformerTranslator;
 
+import ai.djl.modality.Input;
 import ai.djl.modality.Output;
 import ai.djl.ndarray.NDArray;
 import ai.djl.ndarray.NDList;
 import ai.djl.translate.TranslatorContext;
 
 public class SparseEncodingTranslator extends SentenceTransformerTranslator {
+
+    @Override
+    public NDList processInput(TranslatorContext ctx, Input input) {
+        String sparse_encoding_format = input.getAsString(SPARSE_ENCODING_FORMAT_FIELD);
+        if (sparse_encoding_format != null) {
+            ctx.setAttachment(SPARSE_ENCODING_FORMAT_FIELD, sparse_encoding_format);
+        }
+        return super.processInput(ctx, input);
+    }
+
     @Override
     public Output processOutput(TranslatorContext ctx, NDList list) {
         Output output = new Output(200, "OK");
+        Object sparseEncodingFormatObject = ctx.getAttachment(SPARSE_ENCODING_FORMAT_FIELD);
+        String sparseEncodingFormatString = sparseEncodingFormatObject != null
+            ? sparseEncodingFormatObject.toString()
+            : SparseEncodingParameters.SparseEncodingFormat.WORD.name();
 
         List<ModelTensor> outputs = new ArrayList<>();
-        Iterator<NDArray> iterator = list.iterator();
-        while (iterator.hasNext()) {
-            NDArray ndArray = iterator.next();
+        for (NDArray ndArray : list) {
             String name = ndArray.getName();
-            Map<String, Float> tokenWeightsMap = convertOutput(ndArray);
+            Map<String, Float> tokenWeightsMap = convertOutput(ndArray, sparseEncodingFormatString);
             Map<String, ?> wrappedMap = Map.of(ML_MAP_RESPONSE_KEY, Collections.singletonList(tokenWeightsMap));
             ModelTensor tensor = ModelTensor.builder().name(name).dataAsMap(wrappedMap).build();
             outputs.add(tensor);
@@ -44,12 +58,14 @@ public Output processOutput(TranslatorContext ctx, NDList list) {
         return output;
     }
 
-    private Map<String, Float> convertOutput(NDArray array) {
+    private Map<String, Float> convertOutput(NDArray array, String sparseEncodingFormat) {
         Map<String, Float> map = new HashMap<>();
         NDArray nonZeroIndices = array.nonzero().squeeze();
 
         for (long index : nonZeroIndices.toLongArray()) {
-            String s = this.tokenizer.decode(new long[] { index }, true);
+            String s = sparseEncodingFormat.equals(SparseEncodingParameters.SparseEncodingFormat.INT.name())
+                ? Long.toString(index)
+                : this.tokenizer.decode(new long[] { index }, true);
             if (!s.isEmpty()) {
                 map.put(s, array.getFloat(index));
             }

plugin/src/main/java/org/opensearch/ml/plugin/MachineLearningPlugin.java

@@ -135,6 +135,7 @@
 import org.opensearch.ml.common.input.parameter.regression.LogisticRegressionParams;
 import org.opensearch.ml.common.input.parameter.sample.SampleAlgoParams;
 import org.opensearch.ml.common.input.parameter.textembedding.AsymmetricTextEmbeddingParameters;
+import org.opensearch.ml.common.input.parameter.textembedding.SparseEncodingParameters;
 import org.opensearch.ml.common.model.TextEmbeddingModelConfig;
 import org.opensearch.ml.common.settings.MLCommonsSettings;
 import org.opensearch.ml.common.settings.MLFeatureEnabledSetting;
@@ -1038,7 +1039,8 @@ public List<NamedXContentRegistry.Entry> getNamedXContent() {
                 RCFSummarizeParams.XCONTENT_REGISTRY,
                 LogisticRegressionParams.XCONTENT_REGISTRY,
                 TextEmbeddingModelConfig.XCONTENT_REGISTRY,
-                AsymmetricTextEmbeddingParameters.XCONTENT_REGISTRY
+                AsymmetricTextEmbeddingParameters.XCONTENT_REGISTRY,
+                SparseEncodingParameters.XCONTENT_REGISTRY
             );
     }