diff --git a/common/src/main/java/org/opensearch/ml/common/CommonValue.java b/common/src/main/java/org/opensearch/ml/common/CommonValue.java index dab60ce986..3b981bcf20 100644 --- a/common/src/main/java/org/opensearch/ml/common/CommonValue.java +++ b/common/src/main/java/org/opensearch/ml/common/CommonValue.java @@ -42,6 +42,7 @@ public class CommonValue { public static final Integer ML_CONNECTOR_SCHEMA_VERSION = 2; public static final String ML_CONFIG_INDEX = ".plugins-ml-config"; public static final Integer ML_CONFIG_INDEX_SCHEMA_VERSION = 2; + public static final String ML_MAP_RESPONSE_KEY = "response"; public static final String USER_FIELD_MAPPING = " \"" + CommonValue.USER + "\": {\n" diff --git a/common/src/main/java/org/opensearch/ml/common/FunctionName.java b/common/src/main/java/org/opensearch/ml/common/FunctionName.java index 2f6c4ef94d..96c6a58235 100644 --- a/common/src/main/java/org/opensearch/ml/common/FunctionName.java +++ b/common/src/main/java/org/opensearch/ml/common/FunctionName.java @@ -17,6 +17,8 @@ public enum FunctionName { RCF_SUMMARIZE, LOGISTIC_REGRESSION, TEXT_EMBEDDING, + SPARSE_ENCODING, + SPARSE_TOKENIZE, METRICS_CORRELATION, REMOTE; @@ -33,7 +35,7 @@ public static FunctionName from(String value) { * @return true for deep learning model. */ public static boolean isDLModel(FunctionName functionName) { - if (functionName == TEXT_EMBEDDING) { + if (functionName == TEXT_EMBEDDING || functionName == SPARSE_ENCODING || functionName == SPARSE_TOKENIZE) { return true; } return false; diff --git a/common/src/main/java/org/opensearch/ml/common/connector/AbstractConnector.java b/common/src/main/java/org/opensearch/ml/common/connector/AbstractConnector.java index 15e43fd222..5fa213db99 100644 --- a/common/src/main/java/org/opensearch/ml/common/connector/AbstractConnector.java +++ b/common/src/main/java/org/opensearch/ml/common/connector/AbstractConnector.java @@ -23,6 +23,7 @@ import java.util.Map; import java.util.Optional; +import static org.opensearch.ml.common.CommonValue.ML_MAP_RESPONSE_KEY; import static org.opensearch.ml.common.utils.StringUtils.isJson; @Getter @@ -101,7 +102,7 @@ public void parseResponse(T response, List modelTensors, boolea return; } if (response instanceof String && isJson((String)response)) { - Map data = StringUtils.fromJson((String) response, "response"); + Map data = StringUtils.fromJson((String) response, ML_MAP_RESPONSE_KEY); modelTensors.add(ModelTensor.builder().name("response").dataAsMap(data).build()); } else { Map map = new HashMap<>(); diff --git a/common/src/main/java/org/opensearch/ml/common/input/MLInput.java b/common/src/main/java/org/opensearch/ml/common/input/MLInput.java index 8bbd9630b2..574f13e9c3 100644 --- a/common/src/main/java/org/opensearch/ml/common/input/MLInput.java +++ b/common/src/main/java/org/opensearch/ml/common/input/MLInput.java @@ -239,7 +239,7 @@ public static MLInput parse(XContentParser parser, String inputAlgoName) throws } } MLInputDataset inputDataSet = null; - if (algorithm == FunctionName.TEXT_EMBEDDING) { + if (algorithm == FunctionName.TEXT_EMBEDDING || algorithm == FunctionName.SPARSE_ENCODING || algorithm == FunctionName.SPARSE_TOKENIZE) { ModelResultFilter filter = new ModelResultFilter(returnBytes, returnNumber, targetResponse, targetResponsePositions); inputDataSet = new TextDocsInputDataSet(textDocs, filter); } diff --git a/common/src/main/java/org/opensearch/ml/common/input/nlp/TextDocsMLInput.java b/common/src/main/java/org/opensearch/ml/common/input/nlp/TextDocsMLInput.java index 3a00c93436..ec9e31b3a9 100644 --- a/common/src/main/java/org/opensearch/ml/common/input/nlp/TextDocsMLInput.java +++ b/common/src/main/java/org/opensearch/ml/common/input/nlp/TextDocsMLInput.java @@ -25,7 +25,7 @@ * 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}) +@org.opensearch.ml.common.annotation.MLInput(functionNames = {FunctionName.TEXT_EMBEDDING, FunctionName.SPARSE_ENCODING, FunctionName.SPARSE_TOKENIZE}) public class TextDocsMLInput extends MLInput { public static final String TEXT_DOCS_FIELD = "text_docs"; public static final String RESULT_FILTER_FIELD = "result_filter"; diff --git a/common/src/main/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponse.java b/common/src/main/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponse.java index a37ac71e8a..ddf0104c9e 100644 --- a/common/src/main/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponse.java +++ b/common/src/main/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponse.java @@ -12,31 +12,37 @@ import org.opensearch.core.xcontent.ToXContent; import org.opensearch.core.xcontent.ToXContentObject; import org.opensearch.core.xcontent.XContentBuilder; +import org.opensearch.ml.common.MLTaskType; import java.io.IOException; @Getter public class MLDeployModelResponse extends ActionResponse implements ToXContentObject { public static final String TASK_ID_FIELD = "task_id"; + public static final String TASK_TYPE_FIELD = "task_type"; public static final String STATUS_FIELD = "status"; private String taskId; + private MLTaskType taskType; private String status; public MLDeployModelResponse(StreamInput in) throws IOException { super(in); this.taskId = in.readString(); + this.taskType = in.readEnum(MLTaskType.class); this.status = in.readString(); } - public MLDeployModelResponse(String taskId, String status) { + public MLDeployModelResponse(String taskId, MLTaskType mlTaskType, String status) { this.taskId = taskId; + this.taskType = mlTaskType; this.status= status; } @Override public void writeTo(StreamOutput out) throws IOException { out.writeString(taskId); + out.writeEnum(taskType); out.writeString(status); } @@ -44,6 +50,9 @@ public void writeTo(StreamOutput out) throws IOException { public XContentBuilder toXContent(XContentBuilder builder, ToXContent.Params params) throws IOException { builder.startObject(); builder.field(TASK_ID_FIELD, taskId); + if (taskType != null) { + builder.field(TASK_TYPE_FIELD, taskType); + } builder.field(STATUS_FIELD, status); builder.endObject(); return builder; diff --git a/common/src/main/java/org/opensearch/ml/common/transport/register/MLRegisterModelInput.java b/common/src/main/java/org/opensearch/ml/common/transport/register/MLRegisterModelInput.java index a763e9f2e4..e79a09c5b2 100644 --- a/common/src/main/java/org/opensearch/ml/common/transport/register/MLRegisterModelInput.java +++ b/common/src/main/java/org/opensearch/ml/common/transport/register/MLRegisterModelInput.java @@ -15,7 +15,6 @@ import org.opensearch.core.xcontent.XContentParser; import org.opensearch.ml.common.AccessMode; import org.opensearch.ml.common.FunctionName; -import org.opensearch.ml.common.MLCommonsClassLoader; import org.opensearch.ml.common.connector.Connector; import org.opensearch.ml.common.model.MLModelConfig; import org.opensearch.ml.common.model.MLModelFormat; @@ -104,7 +103,7 @@ public MLRegisterModelInput(FunctionName functionName, if (modelFormat == null) { throw new IllegalArgumentException("model format is null"); } - if (url != null && modelConfig == null) { + if (url != null && modelConfig == null && functionName != FunctionName.SPARSE_TOKENIZE && functionName != FunctionName.SPARSE_ENCODING) { // The tokenize model doesn't require a model configuration. Currently, we only support one type of sparse model, which is pretrained, and it doesn't necessitate a model configuration. throw new IllegalArgumentException("model config is null"); } } diff --git a/common/src/main/java/org/opensearch/ml/common/transport/upload_chunk/MLRegisterModelMetaInput.java b/common/src/main/java/org/opensearch/ml/common/transport/upload_chunk/MLRegisterModelMetaInput.java index 0fce0053d1..d23f00caf7 100644 --- a/common/src/main/java/org/opensearch/ml/common/transport/upload_chunk/MLRegisterModelMetaInput.java +++ b/common/src/main/java/org/opensearch/ml/common/transport/upload_chunk/MLRegisterModelMetaInput.java @@ -84,7 +84,7 @@ public MLRegisterModelMetaInput(String name, FunctionName functionName, String m if (modelContentHashValue == null) { throw new IllegalArgumentException("model content hash value is null"); } - if (modelConfig == null) { + if (modelConfig == null && functionName != FunctionName.SPARSE_TOKENIZE && functionName != FunctionName.SPARSE_ENCODING) { // The tokenize model doesn't require a model configuration. Currently, we only support one type of sparse model, which is pretrained, and it doesn't necessitate a model configuration. throw new IllegalArgumentException("model config is null"); } if (totalChunks == null) { diff --git a/common/src/test/java/org/opensearch/ml/common/MLCommonsClassLoaderTests.java b/common/src/test/java/org/opensearch/ml/common/MLCommonsClassLoaderTests.java index 94cba5295f..f8884f11fd 100644 --- a/common/src/test/java/org/opensearch/ml/common/MLCommonsClassLoaderTests.java +++ b/common/src/test/java/org/opensearch/ml/common/MLCommonsClassLoaderTests.java @@ -149,21 +149,27 @@ public void testClassLoader_ExecuteOutputMCorr() throws IOException { assertArrayEquals(new long[]{1, 2}, metrics); } - @Test - public void testClassLoader_MLInput() throws IOException { - assertTrue(MLCommonsClassLoader.canInitMLInput(FunctionName.TEXT_EMBEDDING)); + private void testClassLoader_MLInput_DlModel(FunctionName functionName) throws IOException { + assertTrue(MLCommonsClassLoader.canInitMLInput(functionName)); String jsonStr = "{\"text_docs\":[\"doc1\",\"doc2\"],\"result_filter\":{\"return_bytes\":true,\"return_number\":true,\"target_response\":[\"field1\"], \"target_response_positions\": [2]}}"; XContentParser parser = XContentType.JSON.xContent().createParser(new NamedXContentRegistry(new SearchModule(Settings.EMPTY, Collections.emptyList()).getNamedXContents()), null, jsonStr); parser.nextToken(); - TextDocsMLInput mlInput = MLCommonsClassLoader.initMLInput(FunctionName.TEXT_EMBEDDING, new Object[]{parser, FunctionName.TEXT_EMBEDDING}, XContentParser.class, FunctionName.class); + TextDocsMLInput mlInput = MLCommonsClassLoader.initMLInput(functionName, new Object[]{parser, functionName}, XContentParser.class, FunctionName.class); assertNotNull(mlInput); - assertEquals(FunctionName.TEXT_EMBEDDING, mlInput.getFunctionName()); + assertEquals(functionName, mlInput.getFunctionName()); assertEquals(2, ((TextDocsInputDataSet)mlInput.getInputDataset()).getDocs().size()); } + @Test + public void testClassLoader_MLInput() throws IOException { + testClassLoader_MLInput_DlModel(FunctionName.TEXT_EMBEDDING); + testClassLoader_MLInput_DlModel(FunctionName.SPARSE_TOKENIZE); + testClassLoader_MLInput_DlModel(FunctionName.SPARSE_ENCODING); + } + public enum TestEnum { TEST } diff --git a/common/src/test/java/org/opensearch/ml/common/input/MLInputTest.java b/common/src/test/java/org/opensearch/ml/common/input/MLInputTest.java index e836de34b3..cee7956ce8 100644 --- a/common/src/test/java/org/opensearch/ml/common/input/MLInputTest.java +++ b/common/src/test/java/org/opensearch/ml/common/input/MLInputTest.java @@ -10,11 +10,9 @@ import org.junit.Rule; import org.junit.Test; import org.junit.rules.ExpectedException; -import org.opensearch.core.common.Strings; import org.opensearch.common.io.stream.BytesStreamOutput; import org.opensearch.core.common.io.stream.StreamInput; import org.opensearch.common.settings.Settings; -import org.opensearch.common.xcontent.XContentFactory; import org.opensearch.common.xcontent.XContentType; import org.opensearch.core.xcontent.MediaTypeRegistry; import org.opensearch.core.xcontent.NamedXContentRegistry; @@ -110,8 +108,7 @@ public void parse_LinearRegression() throws IOException { }); } - @Test - public void parse_TextEmbedding() throws IOException { + private void parse_NLPModel(FunctionName functionName) throws IOException { String sentence = "test sentence"; String column = "column1"; Integer position = 1; @@ -119,10 +116,11 @@ public void parse_TextEmbedding() throws IOException { .targetResponse(Arrays.asList(column)) .targetResponsePositions(Arrays.asList(position)) .build(); - TextDocsInputDataSet inputDataset = TextDocsInputDataSet.builder().docs(Arrays.asList(sentence)) - .resultFilter(resultFilter).build(); - String expectedInputStr = "{\"algorithm\":\"TEXT_EMBEDDING\",\"text_docs\":[\"test sentence\"],\"return_bytes\":false,\"return_number\":false,\"target_response\":[\"column1\"],\"target_response_positions\":[1]}"; - testParse(FunctionName.TEXT_EMBEDDING, inputDataset, expectedInputStr, parsedInput -> { + + TextDocsInputDataSet inputDataset = TextDocsInputDataSet.builder().docs(Arrays.asList(sentence)).resultFilter(resultFilter).build(); + String expectedInputStr = "{\"algorithm\":\"functionName\",\"text_docs\":[\"test sentence\"],\"return_bytes\":false,\"return_number\":false,\"target_response\":[\"column1\"],\"target_response_positions\":[1]}"; + expectedInputStr = expectedInputStr.replace("functionName", functionName.toString()); + testParse(functionName, inputDataset, expectedInputStr, parsedInput -> { assertNotNull(parsedInput.getInputDataset()); TextDocsInputDataSet parsedInputDataSet = (TextDocsInputDataSet) parsedInput.getInputDataset(); assertEquals(1, parsedInputDataSet.getDocs().size()); @@ -134,19 +132,33 @@ public void parse_TextEmbedding() throws IOException { } @Test - public void parse_TextEmbedding_NullResultFilter() throws IOException { + public void parse_NLP_Related() throws IOException { + parse_NLPModel(FunctionName.TEXT_EMBEDDING); + parse_NLPModel(FunctionName.SPARSE_TOKENIZE); + parse_NLPModel(FunctionName.SPARSE_ENCODING); + } + + private void parse_NLPModel_NullResultFilter(FunctionName functionName) throws IOException { String sentence = "test sentence"; TextDocsInputDataSet inputDataset = TextDocsInputDataSet.builder().docs(Arrays.asList(sentence)).build(); - String expectedInputStr = "{\"algorithm\":\"TEXT_EMBEDDING\",\"text_docs\":[\"test sentence\"]}"; - testParse(FunctionName.TEXT_EMBEDDING, inputDataset, expectedInputStr, parsedInput -> { + String expectedInputStr = "{\"algorithm\":\"functionName\",\"text_docs\":[\"test sentence\"]}"; + expectedInputStr = expectedInputStr.replace("functionName", functionName.toString()); + testParse(functionName, inputDataset, expectedInputStr, parsedInput -> { assertNotNull(parsedInput.getInputDataset()); assertEquals(1, ((TextDocsInputDataSet) parsedInput.getInputDataset()).getDocs().size()); assertEquals(sentence, ((TextDocsInputDataSet) parsedInput.getInputDataset()).getDocs().get(0)); }); } - private void testParse(FunctionName algorithm, MLInputDataset inputDataset, String expectedInputStr, - Consumer verify) throws IOException { + + @Test + public void parse_NLPRelated_NullResultFilter() throws IOException { + parse_NLPModel_NullResultFilter(FunctionName.TEXT_EMBEDDING); + parse_NLPModel_NullResultFilter(FunctionName.SPARSE_TOKENIZE); + parse_NLPModel_NullResultFilter(FunctionName.SPARSE_ENCODING); + } + + private void testParse(FunctionName algorithm, MLInputDataset inputDataset, String expectedInputStr, Consumer verify) throws IOException { MLInput input = MLInput.builder().inputDataset(inputDataset).algorithm(algorithm).build(); XContentBuilder builder = MediaTypeRegistry.contentBuilder(XContentType.JSON); input.toXContent(builder, ToXContent.EMPTY_PARAMS); diff --git a/common/src/test/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponseTest.java b/common/src/test/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponseTest.java index 854af206c6..ff5792b0a8 100644 --- a/common/src/test/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponseTest.java +++ b/common/src/test/java/org/opensearch/ml/common/transport/deploy/MLDeployModelResponseTest.java @@ -9,6 +9,7 @@ import org.opensearch.core.xcontent.MediaTypeRegistry; import org.opensearch.core.xcontent.ToXContent; import org.opensearch.core.xcontent.XContentBuilder; +import org.opensearch.ml.common.MLTaskType; import java.io.IOException; @@ -19,37 +20,40 @@ public class MLDeployModelResponseTest { private String taskId; private String status; + private MLTaskType taskType; @Before public void setUp() throws Exception { taskId = "test_id"; status = "test"; + taskType = MLTaskType.DEPLOY_MODEL; } @Test public void writeTo_Success() throws IOException { // Setup BytesStreamOutput bytesStreamOutput = new BytesStreamOutput(); - MLDeployModelResponse response = new MLDeployModelResponse(taskId, status); + MLDeployModelResponse response = new MLDeployModelResponse(taskId, taskType, status); // Run the test response.writeTo(bytesStreamOutput); MLDeployModelResponse parsedResponse = new MLDeployModelResponse(bytesStreamOutput.bytes().streamInput()); // Verify the results assertEquals(response.getTaskId(), parsedResponse.getTaskId()); + assertEquals(response.getTaskType(), parsedResponse.getTaskType()); assertEquals(response.getStatus(), parsedResponse.getStatus()); } @Test public void testToXContent() throws IOException { // Setup - MLDeployModelResponse response = new MLDeployModelResponse(taskId, status); + MLDeployModelResponse response = new MLDeployModelResponse(taskId, taskType, status); // Run the test XContentBuilder builder = MediaTypeRegistry.contentBuilder(XContentType.JSON); response.toXContent(builder, ToXContent.EMPTY_PARAMS); assertNotNull(builder); String jsonStr = builder.toString(); // Verify the results - assertEquals("{\"task_id\":\"test_id\"," + + assertEquals("{\"task_id\":\"test_id\"," + "\"task_type\":\"DEPLOY_MODEL\"," + "\"status\":\"test\"}", jsonStr); } } diff --git a/docs/model_serving_framework/text_embedding_model_examples.md b/docs/model_serving_framework/text_embedding_model_examples.md index 5c77f2a584..c13a1ab085 100644 --- a/docs/model_serving_framework/text_embedding_model_examples.md +++ b/docs/model_serving_framework/text_embedding_model_examples.md @@ -239,7 +239,7 @@ GET /_plugins/_ml/profile/models/zwla5YUB1qmVrJFlwzXJ "models": { "zwla5YUB1qmVrJFlwzXJ": { # model id "model_state": "LOADED", - "predictor": "org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel@1a0b0793", + "predictor": "org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel@1a0b0793", "target_worker_nodes": [ # plan to deploy model to these nodes "0TLL4hHxRv6_G3n6y1l0BQ" ], diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/ModelHelper.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/ModelHelper.java index b712b66975..1af6f90990 100644 --- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/ModelHelper.java +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/ModelHelper.java @@ -191,7 +191,7 @@ public List downloadPrebuiltModelMetaList(String taskId, MLRegisterModelInput re * @param modelContentHash model content hash value * @param listener action listener */ - public void downloadAndSplit(MLModelFormat modelFormat, String taskId, String modelName, String version, String url, String modelContentHash, ActionListener> listener) { + public void downloadAndSplit(MLModelFormat modelFormat, String taskId, String modelName, String version, String url, String modelContentHash, FunctionName functionName, ActionListener> listener) { try { AccessController.doPrivileged((PrivilegedExceptionAction) () -> { Path registerModelPath = mlEngine.getRegisterModelPath(taskId, modelName, version); @@ -200,7 +200,7 @@ public void downloadAndSplit(MLModelFormat modelFormat, String taskId, String mo File modelZipFile = new File(modelPath); log.debug("download model to file {}", modelZipFile.getAbsolutePath()); DownloadUtils.download(url, modelPath, new ProgressBar()); - verifyModelZipFile(modelFormat, modelPath, modelName); + verifyModelZipFile(modelFormat, modelPath, modelName, functionName); String hash = calculateFileHash(modelZipFile); if (hash.equals(modelContentHash)) { List chunkFiles = splitFileIntoChunks(modelZipFile, modelPartsPath, CHUNK_SIZE); @@ -222,7 +222,7 @@ public void downloadAndSplit(MLModelFormat modelFormat, String taskId, String mo } } - public void verifyModelZipFile(MLModelFormat modelFormat, String modelZipFilePath, String modelName) throws IOException { + public void verifyModelZipFile(MLModelFormat modelFormat, String modelZipFilePath, String modelName, FunctionName functionName) throws IOException { boolean hasPtFile = false; boolean hasOnnxFile = false; boolean hasTokenizerFile = false; @@ -237,7 +237,7 @@ public void verifyModelZipFile(MLModelFormat modelFormat, String modelZipFilePat } } } - if (!hasPtFile && !hasOnnxFile) { + if (!hasPtFile && !hasOnnxFile && functionName != FunctionName.SPARSE_TOKENIZE) { // sparse tokenizer model doesn't need model file. throw new IllegalArgumentException("Can't find model file"); } if (!hasTokenizerFile) { diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/DLModel.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/DLModel.java index dbad1d982b..2409c6e42a 100644 --- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/DLModel.java +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/DLModel.java @@ -7,11 +7,13 @@ import ai.djl.Application; import ai.djl.Device; +import ai.djl.MalformedModelException; import ai.djl.engine.Engine; import ai.djl.inference.Predictor; import ai.djl.modality.Input; import ai.djl.modality.Output; import ai.djl.repository.zoo.Criteria; +import ai.djl.repository.zoo.ModelNotFoundException; import ai.djl.repository.zoo.ZooModel; import ai.djl.translate.TranslateException; import ai.djl.translate.Translator; @@ -34,6 +36,7 @@ import org.opensearch.ml.engine.utils.ZipUtils; import java.io.File; +import java.io.IOException; import java.nio.file.Path; import java.security.AccessController; import java.security.PrivilegedActionException; @@ -76,10 +79,10 @@ public MLOutput predict(MLInput mlInput) { try { return AccessController.doPrivileged((PrivilegedExceptionAction) () -> { Thread.currentThread().setContextClassLoader(getClass().getClassLoader()); - if (predictors == null) { + if (!isModelReady()) { throw new MLException("model not deployed."); } - return predict(modelId, mlInput); + return this.predict(modelId, mlInput); }); } catch (Throwable e) { String errorMsg = "Failed to inference " + mlInput.getAlgorithm() + " model: " + modelId; @@ -175,6 +178,58 @@ public Map getArguments(MLModelConfig modelConfig) { public void warmUp(Predictor predictor, String modelId, MLModelConfig modelConfig) throws TranslateException {} + protected void doLoadModel(List> predictorList, List> modelList, + String engine, + Path modelPath, + MLModelConfig modelConfig) throws ModelNotFoundException, MalformedModelException, IOException, TranslateException { + devices = Engine.getEngine(engine).getDevices(); + for (int i = 0; i < devices.length; i++) { + log.debug("load model {} to device {}: {}", modelId, i, devices[i]); + ZooModel model; + Predictor predictor; + Criteria.Builder criteriaBuilder = Criteria.builder() + .setTypes(Input.class, Output.class) + .optApplication(Application.UNDEFINED) + .optEngine(engine) + .optDevice(devices[i]) + .optModelPath(modelPath); + Translator translator = getTranslator(engine, modelConfig); + TranslatorFactory translatorFactory = getTranslatorFactory(engine, modelConfig); + if (translatorFactory != null) { + criteriaBuilder.optTranslatorFactory(translatorFactory); + } else if (translator != null) { + criteriaBuilder.optTranslator(translator); + } + + Map arguments = getArguments(modelConfig); + if (arguments != null && arguments.size() > 0) { + for (Map.Entry entry : arguments.entrySet()) { + criteriaBuilder.optArgument(entry.getKey(), entry.getValue()); + } + } + + Criteria criteria = criteriaBuilder.build(); + model = criteria.loadModel(); + predictor = model.newPredictor(); + predictorList.add(predictor); + modelList.add(model); + + // First request takes longer time. Predict once to warm up model. + warmUp(predictor, modelId, modelConfig); + } + + + if (predictorList.size() > 0) { + this.predictors = predictorList.toArray(new Predictor[0]); + predictorList.clear(); + } + if (modelList.size() > 0) { + this.models = modelList.toArray(new ZooModel[0]); + modelList.clear(); + } + log.info("Model {} is successfully deployed on {} devices", modelId, devices.length); + } + protected void loadModel(File modelZipFile, String modelId, String modelName, String version, MLModelConfig modelConfig, String engine) { @@ -217,48 +272,7 @@ protected void loadModel(File modelZipFile, String modelId, String modelName, St } } } - devices = Engine.getEngine(engine).getDevices(); - for (int i = 0; i < devices.length; i++) { - log.debug("load model {} to device {}: {}", modelId, i, devices[i]); - Criteria.Builder criteriaBuilder = Criteria.builder() - .setTypes(Input.class, Output.class) - .optApplication(Application.UNDEFINED) - .optEngine(engine) - .optDevice(devices[i]) - .optModelPath(modelPath); - Translator translator = getTranslator(engine, modelConfig); - TranslatorFactory translatorFactory = getTranslatorFactory(engine, modelConfig); - if (translatorFactory != null) { - criteriaBuilder.optTranslatorFactory(translatorFactory); - } else if (translator != null) { - criteriaBuilder.optTranslator(translator); - } - - Map arguments = getArguments(modelConfig); - if (arguments != null && arguments.size() > 0) { - for (Map.Entry entry : arguments.entrySet()) { - criteriaBuilder.optArgument(entry.getKey(), entry.getValue()); - } - } - - Criteria criteria = criteriaBuilder.build(); - ZooModel model = criteria.loadModel(); - Predictor predictor = model.newPredictor(); - predictorList.add(predictor); - modelList.add(model); - - // First request takes longer time. Predict once to warm up model. - warmUp(predictor, modelId, modelConfig); - } - if (predictorList.size() > 0) { - this.predictors = predictorList.toArray(new Predictor[0]); - predictorList.clear(); - } - if (modelList.size() > 0) { - this.models = modelList.toArray(new ZooModel[0]); - modelList.clear(); - } - log.info("Model {} is successfully deployed on {} devices", modelId, devices.length); + doLoadModel(predictorList, modelList, engine, modelPath, modelConfig); return null; } catch (Throwable e) { String errorMessage = "Failed to deploy model " + modelId; @@ -317,4 +331,4 @@ public ModelTensors parseModelTensorOutput(Output output, ModelResultFilter resu return tensorOutput; } -} +} \ No newline at end of file diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/SentenceTransformerTranslator.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/SentenceTransformerTranslator.java new file mode 100644 index 0000000000..b02a8c9092 --- /dev/null +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/SentenceTransformerTranslator.java @@ -0,0 +1,62 @@ +package org.opensearch.ml.engine.algorithms; + +import ai.djl.huggingface.tokenizers.Encoding; +import ai.djl.huggingface.tokenizers.HuggingFaceTokenizer; +import ai.djl.modality.Input; +import ai.djl.modality.Output; +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDList; +import ai.djl.ndarray.NDManager; +import ai.djl.ndarray.types.DataType; +import ai.djl.translate.Batchifier; +import ai.djl.translate.ServingTranslator; +import ai.djl.translate.TranslatorContext; +import org.opensearch.ml.common.output.model.MLResultDataType; +import org.opensearch.ml.common.output.model.ModelTensor; +import org.opensearch.ml.common.output.model.ModelTensors; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.file.Path; +import java.util.ArrayList; +import java.util.Iterator; +import java.util.List; +import java.util.Map; + +public abstract class SentenceTransformerTranslator implements ServingTranslator { + protected HuggingFaceTokenizer tokenizer; + + @Override + public Batchifier getBatchifier() { + return Batchifier.STACK; + } + @Override + public void prepare(TranslatorContext ctx) throws IOException { + Path path = ctx.getModel().getModelPath(); + tokenizer = HuggingFaceTokenizer.builder().optPadding(true).optTokenizerPath(path.resolve("tokenizer.json")).build(); + } + + @Override + public NDList processInput(TranslatorContext ctx, Input input) { + String sentence = input.getAsString(0); + NDManager manager = ctx.getNDManager(); + NDList ndList = new NDList(); + Encoding encodings = tokenizer.encode(sentence); + long[] indices = encodings.getIds(); + long[] attentionMask = encodings.getAttentionMask(); + + NDArray indicesArray = manager.create(indices); + indicesArray.setName("input1.input_ids"); + + NDArray attentionMaskArray = manager.create(attentionMask); + attentionMaskArray.setName("input1.attention_mask"); + + ndList.add(indicesArray); + ndList.add(attentionMaskArray); + return ndList; + } + + @Override + public void setArguments(Map arguments) { + } +} diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/TextEmbeddingModel.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/TextEmbeddingModel.java new file mode 100644 index 0000000000..5164b1e951 --- /dev/null +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/TextEmbeddingModel.java @@ -0,0 +1,70 @@ +package org.opensearch.ml.engine.algorithms; + +import ai.djl.inference.Predictor; +import ai.djl.modality.Input; +import ai.djl.modality.Output; +import ai.djl.translate.TranslateException; +import ai.djl.translate.Translator; +import ai.djl.translate.TranslatorFactory; +import org.opensearch.ml.common.dataset.MLInputDataset; +import org.opensearch.ml.common.dataset.TextDocsInputDataSet; +import org.opensearch.ml.common.input.MLInput; +import org.opensearch.ml.common.model.MLModelConfig; +import org.opensearch.ml.common.model.TextEmbeddingModelConfig; +import org.opensearch.ml.common.output.model.ModelResultFilter; +import org.opensearch.ml.common.output.model.ModelTensorOutput; +import org.opensearch.ml.common.output.model.ModelTensors; +import org.opensearch.ml.engine.algorithms.DLModel; + +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +public abstract class TextEmbeddingModel extends DLModel { + @Override + public ModelTensorOutput predict(String modelId, MLInput mlInput) throws TranslateException { + MLInputDataset inputDataSet = mlInput.getInputDataset(); + List tensorOutputs = new ArrayList<>(); + Output output; + TextDocsInputDataSet textDocsInput = (TextDocsInputDataSet) inputDataSet; + ModelResultFilter resultFilter = textDocsInput.getResultFilter(); + for (String doc : textDocsInput.getDocs()) { + Input input = new Input(); + input.add(doc); + output = getPredictor().predict(input); + tensorOutputs.add(parseModelTensorOutput(output, resultFilter)); + } + return new ModelTensorOutput(tensorOutputs); + } + + public void warmUp(Predictor predictor, String modelId, MLModelConfig modelConfig) throws TranslateException { + TextEmbeddingModelConfig textEmbeddingModelConfig = (TextEmbeddingModelConfig) modelConfig; + String warmUpSentence = "warm up sentence"; + if (modelConfig != null) { + Integer modelMaxLength = textEmbeddingModelConfig.getModelMaxLength(); + if (modelMaxLength != null) { + warmUpSentence = "sentence ".repeat(modelMaxLength); + } + } + // First request takes longer time. Predict once to warm up model. + Input input = new Input(); + input.add(warmUpSentence); + predictor.predict(input); + } + + public Map getArguments(MLModelConfig modelConfig) { + Map arguments = new HashMap<>(); + if (modelConfig == null){ + return arguments; + } + TextEmbeddingModelConfig textEmbeddingModelConfig = (TextEmbeddingModelConfig) modelConfig; + Integer modelMaxLength = textEmbeddingModelConfig.getModelMaxLength(); + + if (modelMaxLength != null) { + arguments.put("modelMaxLength", modelMaxLength); + } + return arguments; + } + +} diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/SparseEncodingTranslator.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/SparseEncodingTranslator.java new file mode 100644 index 0000000000..13d1c63dee --- /dev/null +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/SparseEncodingTranslator.java @@ -0,0 +1,61 @@ +/* + * Copyright OpenSearch Contributors + * SPDX-License-Identifier: Apache-2.0 + */ + +package org.opensearch.ml.engine.algorithms.sparse_encoding; + +import ai.djl.modality.Output; +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDList; +import ai.djl.translate.TranslatorContext; +import org.opensearch.ml.common.output.model.ModelTensor; +import org.opensearch.ml.common.output.model.ModelTensors; +import org.opensearch.ml.engine.algorithms.SentenceTransformerTranslator; + +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 static org.opensearch.ml.common.CommonValue.ML_MAP_RESPONSE_KEY; + +public class SparseEncodingTranslator extends SentenceTransformerTranslator { + @Override + public Output processOutput(TranslatorContext ctx, NDList list) { + Output output = new Output(200, "OK"); + + List outputs = new ArrayList<>(); + Iterator iterator = list.iterator(); + while (iterator.hasNext()) { + NDArray ndArray = iterator.next(); + String name = ndArray.getName(); + Map tokenWeightsMap = convertOutput(ndArray); + Map wrappedMap = Map.of(ML_MAP_RESPONSE_KEY, Collections.singletonList(tokenWeightsMap)); + ModelTensor tensor = ModelTensor.builder() + .name(name) + .dataAsMap(wrappedMap) + .build(); + outputs.add(tensor); + } + + ModelTensors modelTensorOutput = new ModelTensors(outputs); + output.add(modelTensorOutput.toBytes()); + return output; + } + private Map convertOutput(NDArray array) + { + Map map = new HashMap<>(); + NDArray nonZeroIndices = array.nonzero().squeeze(); + + for (long index : nonZeroIndices.toLongArray()) { + String s = this.tokenizer.decode(new long[]{index}, true); + if (!s.isEmpty()){ + map.put(s, array.getFloat(index)); + } + } + return map; + } +} diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/TextEmbeddingSparseEncodingModel.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/TextEmbeddingSparseEncodingModel.java new file mode 100644 index 0000000000..795391a741 --- /dev/null +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/sparse_encoding/TextEmbeddingSparseEncodingModel.java @@ -0,0 +1,32 @@ +/* + * Copyright OpenSearch Contributors + * SPDX-License-Identifier: Apache-2.0 + */ + +package org.opensearch.ml.engine.algorithms.sparse_encoding; + +import ai.djl.modality.Input; +import ai.djl.modality.Output; +import ai.djl.translate.Translator; +import ai.djl.translate.TranslatorFactory; +import lombok.extern.log4j.Log4j2; +import org.opensearch.ml.common.FunctionName; +import org.opensearch.ml.common.model.MLModelConfig; +import org.opensearch.ml.engine.algorithms.TextEmbeddingModel; +import org.opensearch.ml.engine.annotation.Function; + +@Log4j2 +@Function(FunctionName.SPARSE_ENCODING) +public class TextEmbeddingSparseEncodingModel extends TextEmbeddingModel { + + @Override + public Translator getTranslator(String engine, MLModelConfig modelConfig) { + return new SparseEncodingTranslator(); + } + + @Override + public TranslatorFactory getTranslatorFactory(String engine, MLModelConfig modelConfig) { + return null; + } + +} diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/HuggingfaceTextEmbeddingServingTranslator.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/HuggingfaceTextEmbeddingServingTranslator.java index 9272d99475..9c06e8bca4 100644 --- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/HuggingfaceTextEmbeddingServingTranslator.java +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/HuggingfaceTextEmbeddingServingTranslator.java @@ -18,7 +18,7 @@ import java.util.Collections; import java.util.List; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.SENTENCE_EMBEDDING; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.SENTENCE_EMBEDDING; public class HuggingfaceTextEmbeddingServingTranslator implements Translator { diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/ONNXSentenceTransformerTextEmbeddingTranslator.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/ONNXSentenceTransformerTextEmbeddingTranslator.java index 26ad7baf0d..d97e463b6a 100644 --- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/ONNXSentenceTransformerTextEmbeddingTranslator.java +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/ONNXSentenceTransformerTextEmbeddingTranslator.java @@ -26,7 +26,7 @@ import java.util.List; import java.util.Map; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.SENTENCE_EMBEDDING; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.SENTENCE_EMBEDDING; public class ONNXSentenceTransformerTextEmbeddingTranslator implements ServingTranslator { private static final int[] AXIS = {0}; diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/SentenceTransformerTextEmbeddingTranslator.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/SentenceTransformerTextEmbeddingTranslator.java index cda11eee77..898c619fce 100644 --- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/SentenceTransformerTextEmbeddingTranslator.java +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/SentenceTransformerTextEmbeddingTranslator.java @@ -19,6 +19,7 @@ import org.opensearch.ml.common.output.model.MLResultDataType; import org.opensearch.ml.common.output.model.ModelTensor; import org.opensearch.ml.common.output.model.ModelTensors; +import org.opensearch.ml.engine.algorithms.SentenceTransformerTranslator; import java.io.IOException; import java.nio.ByteBuffer; @@ -28,39 +29,7 @@ import java.util.List; import java.util.Map; -public class SentenceTransformerTextEmbeddingTranslator implements ServingTranslator { - private HuggingFaceTokenizer tokenizer; - - @Override - public Batchifier getBatchifier() { - return Batchifier.STACK; - } - @Override - public void prepare(TranslatorContext ctx) throws IOException { - Path path = ctx.getModel().getModelPath(); - tokenizer = HuggingFaceTokenizer.builder().optPadding(true).optTokenizerPath(path.resolve("tokenizer.json")).build(); - } - - @Override - public NDList processInput(TranslatorContext ctx, Input input) { - String sentence = input.getAsString(0); - NDManager manager = ctx.getNDManager(); - NDList ndList = new NDList(); - Encoding encodings = tokenizer.encode(sentence); - long[] indices = encodings.getIds(); - long[] attentionMask = encodings.getAttentionMask(); - - NDArray indicesArray = manager.create(indices); - indicesArray.setName("input1.input_ids"); - - NDArray attentionMaskArray = manager.create(attentionMask); - attentionMaskArray.setName("input1.attention_mask"); - - ndList.add(indicesArray); - ndList.add(attentionMaskArray); - return ndList; - } - +public class SentenceTransformerTextEmbeddingTranslator extends SentenceTransformerTranslator { @Override public Output processOutput(TranslatorContext ctx, NDList list) { Output output = new Output(200, "OK"); @@ -89,8 +58,4 @@ public Output processOutput(TranslatorContext ctx, NDList list) { output.add(modelTensorOutput.toBytes()); return output; } - - @Override - public void setArguments(Map arguments) { - } } diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingModel.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingDenseModel.java similarity index 55% rename from ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingModel.java rename to ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingDenseModel.java index 9f971588fd..145ae7c1ec 100644 --- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingModel.java +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingDenseModel.java @@ -13,20 +13,12 @@ import ai.djl.translate.TranslatorFactory; import lombok.extern.log4j.Log4j2; import org.opensearch.ml.common.FunctionName; -import org.opensearch.ml.common.dataset.MLInputDataset; -import org.opensearch.ml.common.dataset.TextDocsInputDataSet; -import org.opensearch.ml.common.input.MLInput; import org.opensearch.ml.common.model.MLModelConfig; import org.opensearch.ml.common.model.TextEmbeddingModelConfig; -import org.opensearch.ml.common.output.model.ModelResultFilter; -import org.opensearch.ml.common.output.model.ModelTensorOutput; -import org.opensearch.ml.common.output.model.ModelTensors; -import org.opensearch.ml.engine.algorithms.DLModel; +import org.opensearch.ml.engine.algorithms.TextEmbeddingModel; import org.opensearch.ml.engine.annotation.Function; -import java.util.ArrayList; import java.util.HashMap; -import java.util.List; import java.util.Map; import static org.opensearch.ml.common.model.TextEmbeddingModelConfig.FrameworkType.SENTENCE_TRANSFORMERS; @@ -35,26 +27,10 @@ @Log4j2 @Function(FunctionName.TEXT_EMBEDDING) -public class TextEmbeddingModel extends DLModel { +public class TextEmbeddingDenseModel extends TextEmbeddingModel { public static final String SENTENCE_EMBEDDING = "sentence_embedding"; - @Override - public ModelTensorOutput predict(String modelId, MLInput mlInput) throws TranslateException { - MLInputDataset inputDataSet = mlInput.getInputDataset(); - List tensorOutputs = new ArrayList<>(); - Output output; - TextDocsInputDataSet textDocsInput = (TextDocsInputDataSet) inputDataSet; - ModelResultFilter resultFilter = textDocsInput.getResultFilter(); - for (String doc : textDocsInput.getDocs()) { - Input input = new Input(); - input.add(doc); - output = getPredictor().predict(input); - tensorOutputs.add(parseModelTensorOutput(output, resultFilter)); - } - return new ModelTensorOutput(tensorOutputs); - } - @Override public Translator getTranslator(String engine, MLModelConfig modelConfig) { TextEmbeddingModelConfig textEmbeddingModelConfig = (TextEmbeddingModelConfig) modelConfig; @@ -88,30 +64,4 @@ public TranslatorFactory getTranslatorFactory(String engine, MLModelConfig model } return null; } - - @Override - public Map getArguments(MLModelConfig modelConfig) { - TextEmbeddingModelConfig textEmbeddingModelConfig = (TextEmbeddingModelConfig) modelConfig; - Integer modelMaxLength = textEmbeddingModelConfig.getModelMaxLength(); - Map arguments = new HashMap<>(); - if (modelMaxLength != null) { - arguments.put("modelMaxLength", modelMaxLength); - } - return arguments; - } - - @Override - public void warmUp(Predictor predictor, String modelId, MLModelConfig modelConfig) throws TranslateException { - TextEmbeddingModelConfig textEmbeddingModelConfig = (TextEmbeddingModelConfig) modelConfig; - Integer modelMaxLength = textEmbeddingModelConfig.getModelMaxLength(); - String warmUpSentence = "warm up sentence"; - if (modelMaxLength != null) { - warmUpSentence = "sentence ".repeat(modelMaxLength); - } - // First request takes longer time. Predict once to warm up model. - Input input = new Input(); - input.add(warmUpSentence); - predictor.predict(input); - } - } diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/tokenize/SparseTokenizerModel.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/tokenize/SparseTokenizerModel.java new file mode 100644 index 0000000000..120bf2ab20 --- /dev/null +++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/tokenize/SparseTokenizerModel.java @@ -0,0 +1,142 @@ +/* + * Copyright OpenSearch Contributors + * SPDX-License-Identifier: Apache-2.0 + */ + +package org.opensearch.ml.engine.algorithms.tokenize; + +import ai.djl.MalformedModelException; +import ai.djl.huggingface.tokenizers.Encoding; +import ai.djl.huggingface.tokenizers.HuggingFaceTokenizer; +import ai.djl.inference.Predictor; +import ai.djl.modality.Input; +import ai.djl.modality.Output; +import ai.djl.repository.zoo.ModelNotFoundException; +import ai.djl.repository.zoo.ZooModel; +import ai.djl.translate.TranslateException; +import ai.djl.translate.Translator; +import ai.djl.translate.TranslatorFactory; +import com.google.gson.reflect.TypeToken; +import lombok.extern.log4j.Log4j2; +import org.opensearch.ml.common.FunctionName; +import org.opensearch.ml.common.dataset.MLInputDataset; +import org.opensearch.ml.common.dataset.TextDocsInputDataSet; +import org.opensearch.ml.common.input.MLInput; +import org.opensearch.ml.common.model.MLModelConfig; +import org.opensearch.ml.common.output.model.ModelResultFilter; +import org.opensearch.ml.common.output.model.ModelTensor; +import org.opensearch.ml.common.output.model.ModelTensorOutput; +import org.opensearch.ml.common.output.model.ModelTensors; +import org.opensearch.ml.engine.algorithms.DLModel; +import org.opensearch.ml.engine.annotation.Function; + +import java.io.IOException; +import java.io.InputStreamReader; +import java.lang.reflect.Type; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.stream.Collectors; + +import static org.opensearch.ml.common.CommonValue.ML_MAP_RESPONSE_KEY; +import static org.opensearch.ml.common.utils.StringUtils.gson; + +/** + * Tokenizer model will load from two file: tokenizer file and IDF file. + * For IDF file, it is a predefined weight for each Token. It is calculated like the BM25 IDF for each dataset. + * Decouple idf with model inference will give a more tight upperbound for predicted token weight. And this can help accelerate the WAND algorithm for lucene 9.7. + * IDF introduces global token information, boost search relevance. + * In our pretrained Tokenizer model, we will provide a general IDF from MSMARCO. Customer could recalculate a IDF in their own dataset. + * If without IDF, the weight of each token in the result will be set to 1.0. + * Since we regard tokenizer as a model. Cusotmer needs to keep the consistency between tokenizer/model by themselves. + */ +@Log4j2 +@Function(FunctionName.SPARSE_TOKENIZE) +public class SparseTokenizerModel extends DLModel { + private HuggingFaceTokenizer tokenizer; + + private Map idf; + + public String IDF_FILE_NAME = "idf.json"; + + @Override + public ModelTensorOutput predict(String modelId, MLInput mlInput) throws TranslateException { + MLInputDataset inputDataSet = mlInput.getInputDataset(); + List tensorOutputs = new ArrayList<>(); + TextDocsInputDataSet textDocsInput = (TextDocsInputDataSet) inputDataSet; + ModelResultFilter resultFilter = textDocsInput.getResultFilter(); + for (String doc : textDocsInput.getDocs()) { + Output output = new Output(200, "OK"); + Encoding encodings = tokenizer.encode(doc); + long[] indices = encodings.getIds(); + List outputs = new ArrayList<>(); + String[] tokens = Arrays.stream(indices) + .distinct() + .mapToObj(value -> new long[]{value}) + .map(value -> this.tokenizer.decode(value, true)) + .filter(s -> !s.isEmpty()) + .toArray(String[]::new); + Map tokenWeights = Arrays.stream(tokens) + .collect(Collectors.toMap( + token -> token, + token -> idf.getOrDefault(token, 1.0f) + )); + Map wrappedMap = Map.of(ML_MAP_RESPONSE_KEY, Collections.singletonList(tokenWeights)); + ModelTensor tensor = ModelTensor.builder() + .dataAsMap(wrappedMap) + .build(); + outputs.add(tensor); + ModelTensors modelTensorOutput = new ModelTensors(outputs); + output.add(modelTensorOutput.toBytes()); + tensorOutputs.add(parseModelTensorOutput(output, resultFilter)); + } + return new ModelTensorOutput(tensorOutputs); + } + + protected void doLoadModel(List> predictorList, List> modelList, + String engine, + Path modelPath, + MLModelConfig modelConfig) throws ModelNotFoundException, MalformedModelException, IOException, TranslateException { + tokenizer = HuggingFaceTokenizer.builder().optPadding(true).optTokenizerPath(modelPath.resolve("tokenizer.json")).build(); + idf = new HashMap<>(); + if (Files.exists(modelPath.resolve(IDF_FILE_NAME))){ + Type mapType = new TypeToken>() {}.getType(); + idf = gson.fromJson(new InputStreamReader(Files.newInputStream(modelPath.resolve(IDF_FILE_NAME))), mapType); + } + log.info("sparse tokenize Model {} is successfully deployed", modelId); + } + @Override + public boolean isModelReady() { + if (modelHelper == null || modelId == null || tokenizer == null) { + return false; + } + return true; + } + + @Override + public void close() { + if (modelHelper != null && modelId != null) { + modelHelper.deleteFileCache(modelId); + if (idf != null || tokenizer != null) { + tokenizer = null; + idf = null; + } + } + } + + @Override + public Translator getTranslator(String engine, MLModelConfig modelConfig) { + return null; + } + + @Override + public TranslatorFactory getTranslatorFactory(String engine, MLModelConfig modelConfig) { + return null; + } + +} diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/sparse_encoding/TextEmbeddingSparseEncodingModelTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/sparse_encoding/TextEmbeddingSparseEncodingModelTest.java new file mode 100644 index 0000000000..92d72fd7d4 --- /dev/null +++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/sparse_encoding/TextEmbeddingSparseEncodingModelTest.java @@ -0,0 +1,295 @@ +package org.opensearch.ml.engine.algorithms.sparse_encoding; + +import ai.djl.Model; +import ai.djl.modality.Input; +import ai.djl.modality.Output; +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDList; +import ai.djl.ndarray.NDManager; +import ai.djl.translate.TranslatorContext; +import org.apache.commons.lang3.exception.ExceptionUtils; +import org.junit.After; +import org.junit.Before; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.ExpectedException; +import org.opensearch.ml.common.FunctionName; +import org.opensearch.ml.common.MLModel; +import org.opensearch.ml.common.dataset.TextDocsInputDataSet; +import org.opensearch.ml.common.exception.MLException; +import org.opensearch.ml.common.input.MLInput; +import org.opensearch.ml.common.model.MLModelFormat; +import org.opensearch.ml.common.model.MLModelState; +import org.opensearch.ml.common.output.model.ModelResultFilter; +import org.opensearch.ml.common.output.model.ModelTensor; +import org.opensearch.ml.common.output.model.ModelTensorOutput; +import org.opensearch.ml.common.output.model.ModelTensors; +import org.opensearch.ml.engine.MLEngine; +import org.opensearch.ml.engine.ModelHelper; +import org.opensearch.ml.engine.encryptor.Encryptor; +import org.opensearch.ml.engine.encryptor.EncryptorImpl; +import org.opensearch.ml.engine.utils.FileUtils; + +import java.io.File; +import java.io.IOException; +import java.net.URISyntaxException; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.Iterator; +import java.util.List; +import java.util.Map; +import java.util.UUID; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.mockito.Mockito.*; +import static org.opensearch.ml.engine.algorithms.DLModel.*; + +public class TextEmbeddingSparseEncodingModelTest { + @Rule + public ExpectedException exceptionRule = ExpectedException.none(); + + private File modelZipFile; + private String modelId; + private String modelName; + private FunctionName functionName; + private String version; + private MLModel model; + private ModelHelper modelHelper; + private Map params; + private TextEmbeddingSparseEncodingModel textEmbeddingSparseEncodingModel; + private Path mlCachePath; + private Path mlConfigPath; + private TextDocsInputDataSet inputDataSet; + private int dimension = 384; + private MLEngine mlEngine; + private Encryptor encryptor; + + @Before + public void setUp() throws URISyntaxException { + mlCachePath = Path.of("/tmp/ml_cache" + UUID.randomUUID()); + encryptor = new EncryptorImpl("m+dWmfmnNRiNlOdej/QelEkvMTyH//frS2TBeS2BP4w="); + mlEngine = new MLEngine(mlCachePath, encryptor); + modelId = "test_model_id"; + modelName = "test_model_name"; + functionName = FunctionName.SPARSE_ENCODING; + version = "1"; + model = MLModel.builder() + .modelFormat(MLModelFormat.TORCH_SCRIPT) + .name("test_model_name") + .modelId("test_model_id") + .algorithm(FunctionName.SPARSE_ENCODING) + .version("1.0.0") + .modelState(MLModelState.TRAINED) + .build(); + modelHelper = new ModelHelper(mlEngine); + params = new HashMap<>(); + modelZipFile = new File(getClass().getResource("sparse_demo.zip").toURI()); + params.put(MODEL_ZIP_FILE, modelZipFile); + params.put(MODEL_HELPER, modelHelper); + params.put(ML_ENGINE, mlEngine); + textEmbeddingSparseEncodingModel = new TextEmbeddingSparseEncodingModel(); + + inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("today is sunny", "That is a happy dog")).build(); + } + + @Test + public void test_SparseEncoding_Translator_ProcessInput() throws URISyntaxException, IOException { + SparseEncodingTranslator sparseEncodingTranslator = new SparseEncodingTranslator(); + TranslatorContext translatorContext = mock(TranslatorContext.class); + Model mlModel = mock(Model.class); + when(translatorContext.getModel()).thenReturn(mlModel); + when(mlModel.getModelPath()).thenReturn(Paths.get(getClass().getResource("../tokenize/tokenizer.json").toURI()).getParent()); + sparseEncodingTranslator.prepare(translatorContext); + + NDManager manager = mock(NDManager.class); + when(translatorContext.getNDManager()).thenReturn(manager); + Input input = mock(Input.class); + String testSentence = "hello world"; + when(input.getAsString(0)).thenReturn(testSentence); + NDArray indiceNdArray = mock(NDArray.class); + when(indiceNdArray.toLongArray()).thenReturn(new long[]{102l, 101l}); + when(manager.create((long[]) any())).thenReturn(indiceNdArray); + doNothing().when(indiceNdArray).setName(any()); + NDList outputList = sparseEncodingTranslator.processInput(translatorContext, input); + assertEquals(2, outputList.size()); + Iterator iterator = outputList.iterator(); + while (iterator.hasNext()) { + NDArray ndArray = iterator.next(); + long [] output = ndArray.toLongArray(); + assertEquals(2, output.length); + } + } + + @Test + public void test_SparseEncoding_Translator_ProcessOutput() throws URISyntaxException, IOException { + SparseEncodingTranslator sparseEncodingTranslator = new SparseEncodingTranslator(); + TranslatorContext translatorContext = mock(TranslatorContext.class); + Model mlModel = mock(Model.class); + when(translatorContext.getModel()).thenReturn(mlModel); + when(mlModel.getModelPath()).thenReturn(Paths.get(getClass().getResource("../tokenize/tokenizer.json").toURI()).getParent()); + sparseEncodingTranslator.prepare(translatorContext); + + NDArray ndArray = mock(NDArray.class); + when(ndArray.nonzero()).thenReturn(ndArray); + when(ndArray.squeeze()).thenReturn(ndArray); + when(ndArray.getFloat(any())).thenReturn(1.0f); + when(ndArray.toLongArray()).thenReturn(new long[]{10000, 10001}); + when(ndArray.getName()).thenReturn("output"); + List ndArrayList = Collections.singletonList(ndArray); + NDList ndList = new NDList(ndArrayList); + Output output = sparseEncodingTranslator.processOutput(translatorContext, ndList); + assertNotNull(output); + byte[] bytes = output.getData().getAsBytes(); + ModelTensors tensorOutput = ModelTensors.fromBytes(bytes); + List modelTensorsList = tensorOutput.getMlModelTensors(); + assertEquals(1, modelTensorsList.size()); + ModelTensor modelTensor = modelTensorsList.get(0); + assertEquals("output", modelTensor.getName()); + Map dataAsMap = modelTensor.getDataAsMap(); + assertEquals(1, dataAsMap.size()); + } + + @Test + public void initModel_predict_TorchScript_SparseEncoding() { + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + MLInput mlInput = MLInput.builder().algorithm(FunctionName.SPARSE_ENCODING).inputDataset(inputDataSet).build(); + ModelTensorOutput output = (ModelTensorOutput) textEmbeddingSparseEncodingModel.predict(mlInput); + List mlModelOutputs = output.getMlModelOutputs(); + assertEquals(2, mlModelOutputs.size()); + for (int i=0;i mlModelTensors = tensors.getMlModelTensors(); + assertEquals(1, mlModelTensors.size()); + } + textEmbeddingSparseEncodingModel.close(); + } + + @Test + public void initModel_predict_TorchScript_SparseEncoding_ResultFilter() { + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + ModelResultFilter resultFilter = ModelResultFilter.builder().returnNumber(true).targetResponse(Arrays.asList("output")).build(); + TextDocsInputDataSet textDocsInputDataSet = inputDataSet.toBuilder().resultFilter(resultFilter).build(); + MLInput mlInput = MLInput.builder().algorithm(FunctionName.SPARSE_ENCODING).inputDataset(textDocsInputDataSet).build(); + ModelTensorOutput output = (ModelTensorOutput) textEmbeddingSparseEncodingModel.predict(mlInput); + List mlModelOutputs = output.getMlModelOutputs(); + assertEquals(2, mlModelOutputs.size()); + for (int i=0;i mlModelTensors = tensors.getMlModelTensors(); + assertEquals(1, mlModelTensors.size()); + } + textEmbeddingSparseEncodingModel.close(); + } + + @Test + public void initModel_NullModelZipFile() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model file is null"); + Map params = new HashMap<>(); + params.put(MODEL_HELPER, modelHelper); + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_NullModelHelper() throws URISyntaxException { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model helper is null"); + Map params = new HashMap<>(); + params.put(MODEL_ZIP_FILE, new File(getClass().getResource("sparse_demo.zip").toURI())); + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_NullMLEngine() throws URISyntaxException { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("ML engine is null"); + Map params = new HashMap<>(); + params.put(MODEL_ZIP_FILE, new File(getClass().getResource("sparse_demo.zip").toURI())); + params.put(MODEL_HELPER, modelHelper); + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_NullModelId() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model id is null"); + model.setModelId(null); + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_WrongModelFile() throws URISyntaxException { + try { + Map params = new HashMap<>(); + params.put(MODEL_HELPER, modelHelper); + params.put(MODEL_ZIP_FILE, new File(getClass().getResource("../text_embedding/wrong_zip_with_2_pt_file.zip").toURI())); + params.put(ML_ENGINE, mlEngine); + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + } catch (Exception e) { + assertEquals(MLException.class, e.getClass()); + Throwable rootCause = ExceptionUtils.getRootCause(e); + assertEquals(IllegalArgumentException.class, rootCause.getClass()); + assertEquals("found multiple models", rootCause.getMessage()); + } + } + + @Test + public void initModel_WrongFunctionName() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("wrong function name"); + MLModel mlModel = model.toBuilder().algorithm(FunctionName.KMEANS).build(); + textEmbeddingSparseEncodingModel.initModel(mlModel, params, encryptor); + } + + @Test + public void predict_NullModelHelper() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model not deployed"); + textEmbeddingSparseEncodingModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_ENCODING).inputDataset(inputDataSet).build()); + } + + @Test + public void predict_NullModelId() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model not deployed"); + model.setModelId(null); + try { + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + } catch (Exception e) { + assertEquals("model id is null", e.getMessage()); + } + textEmbeddingSparseEncodingModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_ENCODING).inputDataset(inputDataSet).build()); + } + + @Test + public void predict_AfterModelClosed() { + exceptionRule.expect(MLException.class); + exceptionRule.expectMessage("Failed to inference SPARSE_ENCODING"); + textEmbeddingSparseEncodingModel.initModel(model, params, encryptor); + textEmbeddingSparseEncodingModel.close(); + textEmbeddingSparseEncodingModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_ENCODING).inputDataset(inputDataSet).build()); + } + + @Test + public void parseModelTensorOutput_NullOutput() { + exceptionRule.expect(MLException.class); + exceptionRule.expectMessage("No output generated"); + textEmbeddingSparseEncodingModel.parseModelTensorOutput(null, null); + } + + @Test + public void predict_BeforeInitingModel() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model not deployed"); + textEmbeddingSparseEncodingModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_ENCODING).inputDataset(inputDataSet).build(), model); + } + + @After + public void tearDown() { + FileUtils.deleteFileQuietly(mlCachePath); + } +} diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/ModelHelperTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/ModelHelperTest.java index 0a2768d571..b2f86485e3 100644 --- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/ModelHelperTest.java +++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/ModelHelperTest.java @@ -67,7 +67,7 @@ public void setup() throws URISyntaxException { @Test public void testDownloadAndSplit_UrlFailure() { modelId = "url_failure_model_id"; - modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", "http://testurl", null, actionListener); + modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", "http://testurl", null, FunctionName.TEXT_EMBEDDING, actionListener); ArgumentCaptor argumentCaptor = ArgumentCaptor.forClass(Exception.class); verify(actionListener).onFailure(argumentCaptor.capture()); assertEquals(PrivilegedActionException.class, argumentCaptor.getValue().getClass()); @@ -76,7 +76,7 @@ public void testDownloadAndSplit_UrlFailure() { @Test public void testDownloadAndSplit() throws URISyntaxException { String modelUrl = getClass().getResource("traced_small_model.zip").toURI().toString(); - modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", modelUrl, hashValue, actionListener); + modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", modelUrl, hashValue, FunctionName.TEXT_EMBEDDING, actionListener); ArgumentCaptor argumentCaptor = ArgumentCaptor.forClass(Map.class); verify(actionListener).onResponse(argumentCaptor.capture()); assertNotNull(argumentCaptor.getValue()); @@ -86,7 +86,7 @@ public void testDownloadAndSplit() throws URISyntaxException { @Test public void testDownloadAndSplit_HashFailure() throws URISyntaxException { String modelUrl = getClass().getResource("traced_small_model.zip").toURI().toString(); - modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", modelUrl, "wrong_hash_value", actionListener); + modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", modelUrl, "wrong_hash_value", FunctionName.TEXT_EMBEDDING, actionListener); ArgumentCaptor argumentCaptor = ArgumentCaptor.forClass(Exception.class); verify(actionListener).onFailure(argumentCaptor.capture()); assertEquals(IllegalArgumentException.class, argumentCaptor.getValue().getClass()); @@ -95,7 +95,7 @@ public void testDownloadAndSplit_HashFailure() throws URISyntaxException { @Test public void testDownloadAndSplit_Hash() throws URISyntaxException { String modelUrl = getClass().getResource("traced_small_model.zip").toURI().toString(); - modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", modelUrl, hashValue, actionListener); + modelHelper.downloadAndSplit(modelFormat, modelId, "model_name", "1", modelUrl, hashValue, FunctionName.TEXT_EMBEDDING, actionListener); ArgumentCaptor argumentCaptor = ArgumentCaptor.forClass(Map.class); verify(actionListener).onResponse(argumentCaptor.capture()); assertNotNull(argumentCaptor.getValue()); @@ -105,7 +105,7 @@ public void testDownloadAndSplit_Hash() throws URISyntaxException { @Test public void testVerifyModelZipFile() throws IOException { String modelUrl = getClass().getResource("traced_small_model.zip").toString().substring(5); - modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString()); + modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString(), FunctionName.TEXT_EMBEDDING); } @Test @@ -113,7 +113,7 @@ public void testVerifyModelZipFile_WrongModelFormat_ONNX() throws IOException { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("Model format is TORCH_SCRIPT, but find .onnx file"); String modelUrl = getClass().getResource("traced_small_model_wrong_onnx.zip").toString().substring(5); - modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString()); + modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString(), FunctionName.TEXT_EMBEDDING); } @Test @@ -121,7 +121,7 @@ public void testVerifyModelZipFile_WrongModelFormat_TORCH_SCRIPT() throws IOExce exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("Model format is ONNX, but find .pt file"); String modelUrl = getClass().getResource("traced_small_model_wrong_onnx.zip").toString().substring(5); - modelHelper.verifyModelZipFile(MLModelFormat.ONNX, modelUrl, FunctionName.TEXT_EMBEDDING.toString()); + modelHelper.verifyModelZipFile(MLModelFormat.ONNX, modelUrl, FunctionName.TEXT_EMBEDDING.toString(), FunctionName.TEXT_EMBEDDING); } @Test @@ -129,7 +129,7 @@ public void testVerifyModelZipFile_DuplicateModelFile() throws IOException { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("Find multiple model files, but expected only one"); String modelUrl = getClass().getResource("traced_small_model_duplicate_pt.zip").toString().substring(5); - modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString()); + modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString(), FunctionName.TEXT_EMBEDDING); } @Test @@ -137,7 +137,7 @@ public void testVerifyModelZipFile_MissingTokenizer() throws IOException { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("No tokenizer file"); String modelUrl = getClass().getResource("traced_small_model_missing_tokenizer.zip").toString().substring(5); - modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString()); + modelHelper.verifyModelZipFile(modelFormat, modelUrl, FunctionName.TEXT_EMBEDDING.toString(), FunctionName.TEXT_EMBEDDING); } @Test diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingModelTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingDenseModelTest.java similarity index 85% rename from ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingModelTest.java rename to ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingDenseModelTest.java index b49938b50e..2096841efc 100644 --- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingModelTest.java +++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_embedding/TextEmbeddingDenseModelTest.java @@ -42,13 +42,13 @@ import static org.junit.Assert.assertEquals; import static org.opensearch.ml.common.model.TextEmbeddingModelConfig.FrameworkType.HUGGINGFACE_TRANSFORMERS; import static org.opensearch.ml.common.model.TextEmbeddingModelConfig.FrameworkType.SENTENCE_TRANSFORMERS; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.ML_ENGINE; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.MODEL_HELPER; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.MODEL_ZIP_FILE; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.SENTENCE_EMBEDDING; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.ML_ENGINE; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.MODEL_HELPER; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.MODEL_ZIP_FILE; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.SENTENCE_EMBEDDING; -public class TextEmbeddingModelTest { +public class TextEmbeddingDenseModelTest { @Rule public ExpectedException exceptionRule = ExpectedException.none(); @@ -62,7 +62,7 @@ public class TextEmbeddingModelTest { private MLModel model; private ModelHelper modelHelper; private Map params; - private TextEmbeddingModel textEmbeddingModel; + private TextEmbeddingDenseModel textEmbeddingDenseModel; private Path mlCachePath; private Path mlConfigPath; private TextDocsInputDataSet inputDataSet; @@ -99,7 +99,7 @@ public void setUp() throws URISyntaxException { params.put(MODEL_ZIP_FILE, modelZipFile); params.put(MODEL_HELPER, modelHelper); params.put(ML_ENGINE, mlEngine); - textEmbeddingModel = new TextEmbeddingModel(); + textEmbeddingDenseModel = new TextEmbeddingDenseModel(); inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("today is sunny", "That is a happy dog")).build(); } @@ -112,9 +112,9 @@ public void initModel_predict_TorchScript_SentenceTransformer_SmallModel() throw params.put(ML_ENGINE, mlEngine); TextEmbeddingModelConfig modelConfig = this.modelConfig.toBuilder().embeddingDimension(768).build(); MLModel smallModel = model.toBuilder().modelConfig(modelConfig).build(); - textEmbeddingModel.initModel(smallModel, params, encryptor); + textEmbeddingDenseModel.initModel(smallModel, params, encryptor); MLInput mlInput = MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build(); - ModelTensorOutput output = (ModelTensorOutput)textEmbeddingModel.predict(mlInput); + ModelTensorOutput output = (ModelTensorOutput) textEmbeddingDenseModel.predict(mlInput); List mlModelOutputs = output.getMlModelOutputs(); assertEquals(2, mlModelOutputs.size()); for (int i=0;i mlModelOutputs = output.getMlModelOutputs(); assertEquals(2, mlModelOutputs.size()); for (int i=0;i mlModelOutputs = output.getMlModelOutputs(); assertEquals(2, mlModelOutputs.size()); for (int i=0;i mlModelOutputs = output.getMlModelOutputs(); assertEquals(2, mlModelOutputs.size()); for (int i=0;i params = new HashMap<>(); params.put(MODEL_HELPER, modelHelper); - textEmbeddingModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.initModel(model, params, encryptor); } @Test @@ -242,7 +242,7 @@ public void initModel_NullModelHelper() throws URISyntaxException { exceptionRule.expectMessage("model helper is null"); Map params = new HashMap<>(); params.put(MODEL_ZIP_FILE, new File(getClass().getResource("all-MiniLM-L6-v2_onnx.zip").toURI())); - textEmbeddingModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.initModel(model, params, encryptor); } @Test @@ -252,7 +252,7 @@ public void initModel_NullMLEngine() throws URISyntaxException { Map params = new HashMap<>(); params.put(MODEL_ZIP_FILE, new File(getClass().getResource("all-MiniLM-L6-v2_onnx.zip").toURI())); params.put(MODEL_HELPER, modelHelper); - textEmbeddingModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.initModel(model, params, encryptor); } @Test @@ -260,7 +260,7 @@ public void initModel_NullModelId() { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("model id is null"); model.setModelId(null); - textEmbeddingModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.initModel(model, params, encryptor); } @Test @@ -270,7 +270,7 @@ public void initModel_WrongModelFile() throws URISyntaxException { params.put(MODEL_HELPER, modelHelper); params.put(MODEL_ZIP_FILE, new File(getClass().getResource("wrong_zip_with_2_pt_file.zip").toURI())); params.put(ML_ENGINE, mlEngine); - textEmbeddingModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.initModel(model, params, encryptor); } catch (Exception e) { assertEquals(MLException.class, e.getClass()); Throwable rootCause = ExceptionUtils.getRootCause(e); @@ -284,14 +284,14 @@ public void initModel_WrongFunctionName() { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("wrong function name"); MLModel mlModel = model.toBuilder().algorithm(FunctionName.KMEANS).build(); - textEmbeddingModel.initModel(mlModel, params, encryptor); + textEmbeddingDenseModel.initModel(mlModel, params, encryptor); } @Test public void predict_NullModelHelper() { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("model not deployed"); - textEmbeddingModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build()); + textEmbeddingDenseModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build()); } @Test @@ -300,34 +300,34 @@ public void predict_NullModelId() { exceptionRule.expectMessage("model not deployed"); model.setModelId(null); try { - textEmbeddingModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.initModel(model, params, encryptor); } catch (Exception e) { assertEquals("model id is null", e.getMessage()); } - textEmbeddingModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build()); + textEmbeddingDenseModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build()); } @Test public void predict_AfterModelClosed() { exceptionRule.expect(MLException.class); exceptionRule.expectMessage("Failed to inference TEXT_EMBEDDING"); - textEmbeddingModel.initModel(model, params, encryptor); - textEmbeddingModel.close(); - textEmbeddingModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build()); + textEmbeddingDenseModel.initModel(model, params, encryptor); + textEmbeddingDenseModel.close(); + textEmbeddingDenseModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build()); } @Test public void parseModelTensorOutput_NullOutput() { exceptionRule.expect(MLException.class); exceptionRule.expectMessage("No output generated"); - textEmbeddingModel.parseModelTensorOutput(null, null); + textEmbeddingDenseModel.parseModelTensorOutput(null, null); } @Test public void predict_BeforeInitingModel() { exceptionRule.expect(IllegalArgumentException.class); exceptionRule.expectMessage("model not deployed"); - textEmbeddingModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build(), model); + textEmbeddingDenseModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build(), model); } @After diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/tokenize/SparseTokenizerModelTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/tokenize/SparseTokenizerModelTest.java new file mode 100644 index 0000000000..5134900f23 --- /dev/null +++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/tokenize/SparseTokenizerModelTest.java @@ -0,0 +1,259 @@ +package org.opensearch.ml.engine.algorithms.tokenize; + +import ai.djl.MalformedModelException; +import ai.djl.inference.Predictor; +import ai.djl.modality.Input; +import ai.djl.modality.Output; +import ai.djl.repository.zoo.ModelNotFoundException; +import ai.djl.repository.zoo.ZooModel; +import ai.djl.translate.TranslateException; +import org.apache.commons.lang3.exception.ExceptionUtils; +import org.junit.After; +import org.junit.Before; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.ExpectedException; +import org.opensearch.ml.common.FunctionName; +import org.opensearch.ml.common.MLModel; +import org.opensearch.ml.common.dataset.TextDocsInputDataSet; +import org.opensearch.ml.common.exception.MLException; +import org.opensearch.ml.common.input.MLInput; +import org.opensearch.ml.common.model.MLModelConfig; +import org.opensearch.ml.common.model.MLModelFormat; +import org.opensearch.ml.common.model.MLModelState; +import org.opensearch.ml.common.output.model.ModelTensor; +import org.opensearch.ml.common.output.model.ModelTensorOutput; +import org.opensearch.ml.common.output.model.ModelTensors; +import org.opensearch.ml.engine.MLEngine; +import org.opensearch.ml.engine.ModelHelper; +import org.opensearch.ml.engine.encryptor.Encryptor; +import org.opensearch.ml.engine.encryptor.EncryptorImpl; +import org.opensearch.ml.engine.utils.FileUtils; + +import java.io.File; +import java.io.IOException; +import java.net.URISyntaxException; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.util.*; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.mockito.ArgumentMatchers.any; +import static org.mockito.Mockito.mock; +import static org.mockito.Mockito.when; +import static org.opensearch.ml.engine.algorithms.DLModel.*; +import static org.opensearch.ml.engine.algorithms.DLModel.ML_ENGINE; + +public class SparseTokenizerModelTest { + @Rule + public ExpectedException exceptionRule = ExpectedException.none(); + + private File modelZipFile; + private String modelId; + private String modelName; + private FunctionName functionName; + private String version; + private MLModel model; + private ModelHelper modelHelper; + private Map params; + private SparseTokenizerModel sparseTokenizerModel; + private Path mlCachePath; + private Path mlConfigPath; + private TextDocsInputDataSet inputDataSet; + private MLEngine mlEngine; + private Encryptor encryptor; + + @Before + public void setUp() throws URISyntaxException { + mlCachePath = Path.of("/tmp/ml_cache" + UUID.randomUUID()); + encryptor = new EncryptorImpl("m+dWmfmnNRiNlOdej/QelEkvMTyH//frS2TBeS2BP4w="); + mlEngine = new MLEngine(mlCachePath, encryptor); + modelId = "test_model_id"; + modelName = "test_model_name"; + functionName = FunctionName.SPARSE_TOKENIZE; + version = "1"; + model = MLModel.builder() + .modelFormat(MLModelFormat.TORCH_SCRIPT) + .name("test_model_name") + .modelId("test_model_id") + .algorithm(FunctionName.SPARSE_TOKENIZE) + .version("1.0.0") + .modelState(MLModelState.TRAINED) + .build(); + modelHelper = new ModelHelper(mlEngine); + params = new HashMap<>(); + modelZipFile = new File(getClass().getResource("tokenize-demo.zip").toURI()); + params.put(MODEL_ZIP_FILE, modelZipFile); + params.put(MODEL_HELPER, modelHelper); + params.put(ML_ENGINE, mlEngine); + sparseTokenizerModel = new SparseTokenizerModel(); + + inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("today is sunny", "That is dog")).build(); + } + + @Test + public void test_doLoadModel() throws URISyntaxException, TranslateException, ModelNotFoundException, MalformedModelException, IOException { + SparseTokenizerModel sparseTokenizerModel = mock(SparseTokenizerModel.class); + Predictor predictor = mock(Predictor.class); + List> predictorList = Collections.singletonList(predictor); + ZooModel model = mock(ZooModel.class); + List > modelList = Collections.singletonList(model); + String engine = "engine"; + Path modelPath = mock(Path.class); + when(modelPath.resolve((String) any())).thenReturn(Paths.get(getClass().getResource("tokenizer.json").toURI())); + MLModelConfig modelConfig = mock(MLModelConfig.class); + sparseTokenizerModel.doLoadModel(predictorList, modelList, engine, modelPath, modelConfig); + } + + @Test + public void test_Predict() throws URISyntaxException, TranslateException, ModelNotFoundException, MalformedModelException, IOException { + SparseTokenizerModel sparseTokenizerModel = new SparseTokenizerModel(); + Predictor predictor = mock(Predictor.class); + List> predictorList = Collections.singletonList(predictor); + ZooModel model = mock(ZooModel.class); + List > modelList = Collections.singletonList(model); + String engine = "engine"; + Path modelPath = Paths.get(getClass().getResource("tokenizer.json").toURI()).getParent(); + MLModelConfig modelConfig = mock(MLModelConfig.class); + sparseTokenizerModel.doLoadModel(predictorList, modelList, engine, modelPath, modelConfig); + + MLInput mlInput = mock(MLInput.class); + TextDocsInputDataSet textDocsInputDataSet = mock(TextDocsInputDataSet.class); + when(mlInput.getInputDataset()).thenReturn(textDocsInputDataSet); + + when(textDocsInputDataSet.getResultFilter()).thenReturn(null); + List docs = Collections.singletonList("hello world"); + when(textDocsInputDataSet.getDocs()).thenReturn(docs); + ModelTensorOutput modelTensorOutput = (ModelTensorOutput) sparseTokenizerModel.predict(null, mlInput); + assertNotNull(modelTensorOutput); + List modelTensorsList = modelTensorOutput.getMlModelOutputs(); + assertEquals(1, modelTensorsList.size()); + ModelTensors modelTensors = modelTensorsList.get(0); + List modelTensorList = modelTensors.getMlModelTensors(); + assertEquals(1, modelTensorList.size()); + ModelTensor modelTensor = modelTensorList.get(0); + assertNotNull(modelTensor); + } + + @Test + public void initModel_predict_Tokenize() throws URISyntaxException, TranslateException { + sparseTokenizerModel.initModel(model, params, encryptor); + MLInput mlInput = MLInput.builder().algorithm(FunctionName.SPARSE_TOKENIZE).inputDataset(inputDataSet).build(); + ModelTensorOutput output = (ModelTensorOutput) sparseTokenizerModel.predict(mlInput); + List mlModelOutputs = output.getMlModelOutputs(); + assertEquals(2, mlModelOutputs.size()); + for (int i=0;i mlModelTensors = tensors.getMlModelTensors(); + assertEquals(1, mlModelTensors.size()); + ModelTensor tensor = mlModelTensors.get(0); + Map resultMap = tensor.getDataAsMap(); + assertEquals(resultMap.size(), 1); + List> resultList = (List< Map>) resultMap.get("response"); + assertEquals(resultList.size(), 1); + Map result = resultList.get(0); + assertEquals(result.size(), 3); + } + } + + + @Test + public void initModel_NullModelHelper() throws URISyntaxException { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model helper is null"); + Map params = new HashMap<>(); + params.put(MODEL_ZIP_FILE, new File(getClass().getResource("tokenize-demo.zip").toURI())); + sparseTokenizerModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_NullMLEngine() throws URISyntaxException { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("ML engine is null"); + Map params = new HashMap<>(); + params.put(MODEL_ZIP_FILE, new File(getClass().getResource("tokenize-demo.zip").toURI())); + params.put(MODEL_HELPER, modelHelper); + sparseTokenizerModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_NullModelId() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model id is null"); + model.setModelId(null); + sparseTokenizerModel.initModel(model, params, encryptor); + } + + @Test + public void initModel_WrongModelFile() throws URISyntaxException { + try { + Map params = new HashMap<>(); + params.put(MODEL_HELPER, modelHelper); + params.put(MODEL_ZIP_FILE, new File(getClass().getResource("../text_embedding/wrong_zip_with_2_pt_file.zip").toURI())); + params.put(ML_ENGINE, mlEngine); + sparseTokenizerModel.initModel(model, params, encryptor); + } catch (Exception e) { + assertEquals(MLException.class, e.getClass()); + Throwable rootCause = ExceptionUtils.getRootCause(e); + assertEquals(IllegalArgumentException.class, rootCause.getClass()); + assertEquals("found multiple models", rootCause.getMessage()); + } + } + + @Test + public void initModel_WrongFunctionName() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("wrong function name"); + MLModel mlModel = model.toBuilder().algorithm(FunctionName.KMEANS).build(); + sparseTokenizerModel.initModel(mlModel, params, encryptor); + } + + @Test + public void predict_NullModelHelper() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model not deployed"); + sparseTokenizerModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_TOKENIZE).inputDataset(inputDataSet).build()); + } + + @Test + public void predict_NullModelId() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model not deployed"); + model.setModelId(null); + try { + sparseTokenizerModel.initModel(model, params, encryptor); + } catch (Exception e) { + assertEquals("model id is null", e.getMessage()); + } + sparseTokenizerModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_TOKENIZE).inputDataset(inputDataSet).build()); + } + + @Test + public void predict_AfterModelClosed() throws TranslateException { + exceptionRule.expect(MLException.class); + exceptionRule.expectMessage("Failed to inference SPARSE_TOKENIZE"); + sparseTokenizerModel.initModel(model, params, encryptor); + sparseTokenizerModel.close(); + sparseTokenizerModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_TOKENIZE).inputDataset(inputDataSet).build()); + } + + @Test + public void parseModelTensorOutput_NullOutput() { + exceptionRule.expect(MLException.class); + exceptionRule.expectMessage("No output generated"); + sparseTokenizerModel.parseModelTensorOutput(null, null); + } + + @Test + public void predict_BeforeInitingModel() { + exceptionRule.expect(IllegalArgumentException.class); + exceptionRule.expectMessage("model not deployed"); + sparseTokenizerModel.predict(MLInput.builder().algorithm(FunctionName.SPARSE_TOKENIZE).inputDataset(inputDataSet).build(), model); + } + + @After + public void tearDown() { + FileUtils.deleteFileQuietly(mlCachePath); + } +} diff --git a/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/sparse_encoding/sparse_demo.zip b/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/sparse_encoding/sparse_demo.zip new file mode 100644 index 0000000000..d36ac8a374 Binary files /dev/null and b/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/sparse_encoding/sparse_demo.zip differ diff --git a/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/tokenize/tokenize-demo.zip b/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/tokenize/tokenize-demo.zip new file mode 100644 index 0000000000..4484ad3051 Binary files /dev/null and b/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/tokenize/tokenize-demo.zip differ diff --git a/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/tokenize/tokenizer.json b/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/tokenize/tokenizer.json new file mode 100644 index 0000000000..cb202bfe2e --- /dev/null +++ b/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/tokenize/tokenizer.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/plugin/src/main/java/org/opensearch/ml/action/deploy/TransportDeployModelAction.java b/plugin/src/main/java/org/opensearch/ml/action/deploy/TransportDeployModelAction.java index 134b3260de..54552fc204 100644 --- a/plugin/src/main/java/org/opensearch/ml/action/deploy/TransportDeployModelAction.java +++ b/plugin/src/main/java/org/opensearch/ml/action/deploy/TransportDeployModelAction.java @@ -213,9 +213,14 @@ protected void doExecute(Task task, ActionRequest request, ActionListener { String taskId = response.getId(); mlTask.setTaskId(taskId); + if (algorithm == FunctionName.REMOTE) { + mlTaskManager.add(mlTask, nodeIds); + deployRemoteModel(mlModel, mlTask, localNodeId, eligibleNodes, deployToAllNodes, listener); + return; + } try { mlTaskManager.add(mlTask, nodeIds); - listener.onResponse(new MLDeployModelResponse(taskId, MLTaskState.CREATED.name())); + listener.onResponse(new MLDeployModelResponse(taskId, MLTaskType.DEPLOY_MODEL, MLTaskState.CREATED.name())); threadPool .executor(DEPLOY_THREAD_POOL) .execute( @@ -260,6 +265,82 @@ protected void doExecute(Task task, ActionRequest request, ActionListener eligibleNodes, + boolean deployToAllNodes, + ActionListener listener + ) { + MLDeployModelInput deployModelInput = new MLDeployModelInput( + mlModel.getModelId(), + mlTask.getTaskId(), + mlModel.getModelContentHash(), + eligibleNodes.size(), + localNodeId, + deployToAllNodes, + mlTask + ); + + MLDeployModelNodesRequest deployModelRequest = new MLDeployModelNodesRequest( + eligibleNodes.toArray(new DiscoveryNode[0]), + deployModelInput + ); + + ActionListener actionListener = deployModelNodesResponseListener( + mlTask.getTaskId(), + mlModel.getModelId(), + listener + ); + List workerNodes = eligibleNodes.stream().map(n -> n.getId()).collect(Collectors.toList()); + mlModelManager + .updateModel( + mlModel.getModelId(), + ImmutableMap + .of( + MLModel.MODEL_STATE_FIELD, + MLModelState.DEPLOYING, + MLModel.PLANNING_WORKER_NODE_COUNT_FIELD, + eligibleNodes.size(), + MLModel.PLANNING_WORKER_NODES_FIELD, + workerNodes, + MLModel.DEPLOY_TO_ALL_NODES_FIELD, + deployToAllNodes + ), + ActionListener + .wrap( + r -> client.execute(MLDeployModelOnNodeAction.INSTANCE, deployModelRequest, actionListener), + actionListener::onFailure + ) + ); + } + + private ActionListener deployModelNodesResponseListener( + String taskId, + String modelId, + ActionListener listener + ) { + return ActionListener.wrap(r -> { + if (mlTaskManager.contains(taskId)) { + mlTaskManager.updateMLTask(taskId, ImmutableMap.of(STATE_FIELD, MLTaskState.RUNNING), TASK_SEMAPHORE_TIMEOUT, false); + } + listener.onResponse(new MLDeployModelResponse(taskId, MLTaskType.DEPLOY_MODEL, MLTaskState.COMPLETED.name())); + }, e -> { + log.error("Failed to deploy model " + modelId, e); + mlTaskManager + .updateMLTask( + taskId, + ImmutableMap.of(MLTask.ERROR_FIELD, MLExceptionUtils.getRootCauseMessage(e), STATE_FIELD, FAILED), + TASK_SEMAPHORE_TIMEOUT, + true + ); + mlModelManager.updateModel(modelId, ImmutableMap.of(MLModel.MODEL_STATE_FIELD, MLModelState.DEPLOY_FAILED)); + listener.onFailure(e); + }); + } + @VisibleForTesting void updateModelDeployStatusAndTriggerOnNodesAction( String modelId, diff --git a/plugin/src/main/java/org/opensearch/ml/action/prediction/TransportPredictionTaskAction.java b/plugin/src/main/java/org/opensearch/ml/action/prediction/TransportPredictionTaskAction.java index 89f63a301c..234c361b09 100644 --- a/plugin/src/main/java/org/opensearch/ml/action/prediction/TransportPredictionTaskAction.java +++ b/plugin/src/main/java/org/opensearch/ml/action/prediction/TransportPredictionTaskAction.java @@ -88,6 +88,7 @@ protected void doExecute(Task task, ActionRequest request, ActionListener { FunctionName functionName = mlModel.getAlgorithm(); + mlPredictionTaskRequest.getMlInput().setAlgorithm(functionName); modelAccessControlHelper .validateModelGroupAccess(userInfo, mlModel.getModelGroupId(), client, ActionListener.wrap(access -> { if (!access) { diff --git a/plugin/src/main/java/org/opensearch/ml/action/register/TransportRegisterModelAction.java b/plugin/src/main/java/org/opensearch/ml/action/register/TransportRegisterModelAction.java index 6fcb190dfa..23006f5464 100644 --- a/plugin/src/main/java/org/opensearch/ml/action/register/TransportRegisterModelAction.java +++ b/plugin/src/main/java/org/opensearch/ml/action/register/TransportRegisterModelAction.java @@ -250,7 +250,6 @@ private void registerModel(MLRegisterModelInput registerModelInput, ActionListen mlTaskManager.createMLTask(mlTask, ActionListener.wrap(response -> { String taskId = response.getId(); mlTask.setTaskId(taskId); - System.out.println("mlModelManager calls registerMLRemoteModel"); mlModelManager.registerMLRemoteModel(registerModelInput, mlTask, listener); }, e -> { logException("Failed to register model", e, log); diff --git a/plugin/src/main/java/org/opensearch/ml/model/MLModelManager.java b/plugin/src/main/java/org/opensearch/ml/model/MLModelManager.java index 92c9590e15..a2d962b444 100644 --- a/plugin/src/main/java/org/opensearch/ml/model/MLModelManager.java +++ b/plugin/src/main/java/org/opensearch/ml/model/MLModelManager.java @@ -27,9 +27,9 @@ import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.CLUSTER_SERVICE; import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.SCRIPT_SERVICE; import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.XCONTENT_REGISTRY; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.ML_ENGINE; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.MODEL_HELPER; -import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel.MODEL_ZIP_FILE; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.ML_ENGINE; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.MODEL_HELPER; +import static org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel.MODEL_ZIP_FILE; import static org.opensearch.ml.engine.utils.FileUtils.calculateFileHash; import static org.opensearch.ml.engine.utils.FileUtils.deleteFileQuietly; import static org.opensearch.ml.plugin.MachineLearningPlugin.DEPLOY_THREAD_POOL; @@ -670,6 +670,7 @@ private void registerModel( version, registerModelInput.getUrl(), registerModelInput.getHashValue(), + functionName, ActionListener.wrap(result -> { Long modelSizeInBytes = (Long) result.get(MODEL_SIZE_IN_BYTES); if (modelSizeInBytes >= MODEL_FILE_SIZE_LIMIT) { diff --git a/plugin/src/test/java/org/opensearch/ml/action/profile/MLProfileModelResponseTests.java b/plugin/src/test/java/org/opensearch/ml/action/profile/MLProfileModelResponseTests.java index ca98524590..262cee551c 100644 --- a/plugin/src/test/java/org/opensearch/ml/action/profile/MLProfileModelResponseTests.java +++ b/plugin/src/test/java/org/opensearch/ml/action/profile/MLProfileModelResponseTests.java @@ -106,7 +106,6 @@ public void test_toXContent() throws IOException { XContentBuilder builder = XContentBuilder.builder(XContentType.JSON.xContent()); response.toXContent(builder, ToXContent.EMPTY_PARAMS); String xContentString = TestHelper.xContentBuilderToString(builder); - System.out.println(xContentString); } } diff --git a/plugin/src/test/java/org/opensearch/ml/bwc/MLCommonsBackwardsCompatibilityRestTestCase.java b/plugin/src/test/java/org/opensearch/ml/bwc/MLCommonsBackwardsCompatibilityRestTestCase.java index 4b28c90a3a..ab32d4d91a 100644 --- a/plugin/src/test/java/org/opensearch/ml/bwc/MLCommonsBackwardsCompatibilityRestTestCase.java +++ b/plugin/src/test/java/org/opensearch/ml/bwc/MLCommonsBackwardsCompatibilityRestTestCase.java @@ -731,10 +731,7 @@ public Consumer> verifyTextEmbeddingModelDeployed() { return (modelProfile) -> { if (modelProfile.containsKey("model_state")) { assertEquals(MLModelState.DEPLOYED.name(), modelProfile.get("model_state")); - assertTrue( - ((String) modelProfile.get("predictor")) - .startsWith("org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel@") - ); + assertTrue(((String) modelProfile.get("predictor")).startsWith("org.opensearch.ml.engine.algorithms.TextEmbeddingModel@")); } List workNodes = (List) modelProfile.get("worker_nodes"); assertTrue(workNodes.size() > 0); diff --git a/plugin/src/test/java/org/opensearch/ml/model/MLModelCacheHelperTests.java b/plugin/src/test/java/org/opensearch/ml/model/MLModelCacheHelperTests.java index 733dbca062..78d67b3ce1 100644 --- a/plugin/src/test/java/org/opensearch/ml/model/MLModelCacheHelperTests.java +++ b/plugin/src/test/java/org/opensearch/ml/model/MLModelCacheHelperTests.java @@ -29,7 +29,7 @@ import org.opensearch.ml.common.exception.MLLimitExceededException; import org.opensearch.ml.common.model.MLModelFormat; import org.opensearch.ml.common.model.MLModelState; -import org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel; +import org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingDenseModel; import org.opensearch.ml.profile.MLModelProfile; import org.opensearch.ml.profile.MLPredictRequestStats; import org.opensearch.test.OpenSearchTestCase; @@ -47,7 +47,7 @@ public class MLModelCacheHelperTests extends OpenSearchTestCase { private String modelId; private String nodeId; - private TextEmbeddingModel predictor; + private TextEmbeddingDenseModel predictor; private int maxMonitoringRequests; private List targetWorkerNodes; @@ -65,7 +65,7 @@ public void setup() { modelId = "model_id1"; nodeId = "node_id1"; - predictor = spy(new TextEmbeddingModel()); + predictor = spy(new TextEmbeddingDenseModel()); targetWorkerNodes = new ArrayList<>(); targetWorkerNodes.add(nodeId); } @@ -284,7 +284,7 @@ public void testGetModelProfile() { cacheHelper.addWorkerNode(modelId, nodeId); MLModelProfile modelProfile = cacheHelper.getModelProfile(modelId); assertNotNull(modelProfile); - assertTrue(modelProfile.getPredictor().contains("TextEmbeddingModel")); + assertTrue(modelProfile.getPredictor().contains("TextEmbeddingDenseModel")); assertEquals(MLModelState.DEPLOYED, modelProfile.getModelState()); assertArrayEquals(new String[] { nodeId }, modelProfile.getWorkerNodes()); assertNull(modelProfile.getModelInferenceStats()); diff --git a/plugin/src/test/java/org/opensearch/ml/model/MLModelManagerTests.java b/plugin/src/test/java/org/opensearch/ml/model/MLModelManagerTests.java index 7dc36f41ac..d67f84de25 100644 --- a/plugin/src/test/java/org/opensearch/ml/model/MLModelManagerTests.java +++ b/plugin/src/test/java/org/opensearch/ml/model/MLModelManagerTests.java @@ -325,7 +325,7 @@ public void testRegisterMLModel_InitModelIndexFailure() { modelManager.registerMLModel(registerModelInput, mlTask); verify(mlTaskManager).updateMLTask(anyString(), anyMap(), anyLong(), anyBoolean()); - verify(modelHelper, never()).downloadAndSplit(any(), any(), any(), any(), any(), any(), any()); + verify(modelHelper, never()).downloadAndSplit(any(), any(), any(), any(), any(), any(), any(), any()); verify(client, never()).index(any(), any()); } @@ -340,7 +340,7 @@ public void testRegisterMLModel_IndexModelMetaFailure() { modelManager.registerMLModel(registerModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client).index(any(), any()); - verify(modelHelper, never()).downloadAndSplit(any(), any(), any(), any(), any(), any(), any()); + verify(modelHelper, never()).downloadAndSplit(any(), any(), any(), any(), any(), any(), any(), any()); } @Ignore @@ -356,7 +356,7 @@ public void testRegisterMLModel_IndexModelChunkFailure() throws IOException { modelManager.registerMLModel(registerModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client, times(2)).index(any(), any()); - verify(modelHelper).downloadAndSplit(any(), any(), any(), any(), any(), any(), any()); + verify(modelHelper).downloadAndSplit(any(), any(), any(), any(), any(), any(), any(), any()); } public void testRegisterMLModel_DownloadModelFileFailure() { @@ -370,7 +370,7 @@ public void testRegisterMLModel_DownloadModelFileFailure() { modelManager.registerMLModel(registerModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client).index(any(), any()); - verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any()); + verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any(), any()); } @Ignore @@ -386,7 +386,7 @@ public void testRegisterMLModel_DownloadModelFile() throws IOException { modelManager.registerMLModel(registerModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client, times(3)).index(any(), any()); - verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any()); + verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any(), any()); } @Ignore @@ -404,7 +404,7 @@ public void testRegisterMLModel_DeployModel() throws IOException { modelManager.registerMLModel(mlRegisterModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client, times(3)).index(any(), any()); - verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any()); + verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any(), any()); verify(client).execute(eq(MLDeployModelAction.INSTANCE), any(), any()); } @@ -423,7 +423,7 @@ public void testRegisterMLModel_DeployModel_failure() throws IOException { modelManager.registerMLModel(mlRegisterModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client, times(3)).index(any(), any()); - verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any()); + verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any(), any()); verify(client, never()).execute(eq(MLDeployModelAction.INSTANCE), any(), any()); } @@ -439,7 +439,7 @@ public void testRegisterMLModel_DownloadModelFile_ModelFileSizeExceedLimit() thr modelManager.registerMLModel(registerModelInput, mlTask); verify(mlIndicesHandler).initModelIndexIfAbsent(any()); verify(client, times(1)).index(any(), any()); - verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any()); + verify(modelHelper).downloadAndSplit(eq(modelFormat), eq(modelId), eq(modelName), eq(version), eq(url), any(), any(), any()); } public void testRegisterModel_ClientFailedToGetThreadPool() { @@ -808,7 +808,7 @@ private void setUpMock_DownloadModelFileFailure() { ActionListener> listener = invocation.getArgument(6); listener.onFailure(new RuntimeException("downloadAndSplit failure")); return null; - }).when(modelHelper).downloadAndSplit(any(), any(), any(), any(), any(), any(), any()); + }).when(modelHelper).downloadAndSplit(any(), any(), any(), any(), any(), any(), any(), any()); } private void setUpMock_DownloadModelFile(String[] chunks, Long modelContentSize) { @@ -820,7 +820,7 @@ private void setUpMock_DownloadModelFile(String[] chunks, Long modelContentSize) result.put(MODEL_FILE_HASH, randomAlphaOfLength(10)); listener.onResponse(result); return null; - }).when(modelHelper).downloadAndSplit(any(), any(), any(), any(), any(), any(), any()); + }).when(modelHelper).downloadAndSplit(any(), any(), any(), any(), any(), any(), any(), any()); } @Mock diff --git a/plugin/src/test/java/org/opensearch/ml/rest/MLCommonsRestTestCase.java b/plugin/src/test/java/org/opensearch/ml/rest/MLCommonsRestTestCase.java index d5994bd3c1..4e1c4eeddd 100644 --- a/plugin/src/test/java/org/opensearch/ml/rest/MLCommonsRestTestCase.java +++ b/plugin/src/test/java/org/opensearch/ml/rest/MLCommonsRestTestCase.java @@ -831,10 +831,7 @@ public Consumer> verifyTextEmbeddingModelDeployed() { return (modelProfile) -> { if (modelProfile.containsKey("model_state")) { assertEquals(MLModelState.DEPLOYED.name(), modelProfile.get("model_state")); - assertTrue( - ((String) modelProfile.get("predictor")) - .startsWith("org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel@") - ); + assertTrue(((String) modelProfile.get("predictor")).startsWith("org.opensearch.ml.engine.algorithms.TextEmbeddingModel@")); } List workNodes = (List) modelProfile.get("worker_nodes"); assertTrue(workNodes.size() > 0); diff --git a/plugin/src/test/java/org/opensearch/ml/rest/RestMLRemoteInferenceIT.java b/plugin/src/test/java/org/opensearch/ml/rest/RestMLRemoteInferenceIT.java index 401f1150de..c4f106d134 100644 --- a/plugin/src/test/java/org/opensearch/ml/rest/RestMLRemoteInferenceIT.java +++ b/plugin/src/test/java/org/opensearch/ml/rest/RestMLRemoteInferenceIT.java @@ -129,7 +129,7 @@ public void testDeployRemoteModel() throws IOException, InterruptedException { String modelId = (String) responseMap.get("model_id"); response = deployRemoteModel(modelId); responseMap = parseResponseToMap(response); - assertEquals("CREATED", (String) responseMap.get("status")); + assertEquals("COMPLETED", (String) responseMap.get("status")); taskId = (String) responseMap.get("task_id"); waitForTask(taskId, MLTaskState.COMPLETED); } diff --git a/plugin/src/test/java/org/opensearch/ml/rest/RestMLSearchConnectorActionTests.java b/plugin/src/test/java/org/opensearch/ml/rest/RestMLSearchConnectorActionTests.java index 2886380eba..97ed37230a 100644 --- a/plugin/src/test/java/org/opensearch/ml/rest/RestMLSearchConnectorActionTests.java +++ b/plugin/src/test/java/org/opensearch/ml/rest/RestMLSearchConnectorActionTests.java @@ -137,7 +137,6 @@ public void testPrepareRequest() throws Exception { SearchRequest searchRequest = argumentCaptor.getValue(); String[] indices = searchRequest.indices(); assertArrayEquals(new String[] { ML_CONNECTOR_INDEX }, indices); - System.out.println(searchRequest); assertEquals( "{\"query\":{\"match_all\":{\"boost\":1.0}},\"version\":true,\"seq_no_primary_term\":true,\"_source\":{\"includes\":[],\"excludes\":[\"content\",\"model_content\",\"ui_metadata\"]}}", searchRequest.source().toString()