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MLModelManager.java
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/*
* Copyright OpenSearch Contributors
* SPDX-License-Identifier: Apache-2.0
*/
package org.opensearch.ml.model;
import static org.opensearch.common.xcontent.XContentType.JSON;
import static org.opensearch.common.xcontent.json.JsonXContent.jsonXContent;
import static org.opensearch.core.xcontent.ToXContent.EMPTY_PARAMS;
import static org.opensearch.core.xcontent.XContentParserUtils.ensureExpectedToken;
import static org.opensearch.ml.common.CommonValue.ML_CONNECTOR_INDEX;
import static org.opensearch.ml.common.CommonValue.ML_CONTROLLER_INDEX;
import static org.opensearch.ml.common.CommonValue.ML_MODEL_GROUP_INDEX;
import static org.opensearch.ml.common.CommonValue.ML_MODEL_INDEX;
import static org.opensearch.ml.common.CommonValue.NOT_FOUND;
import static org.opensearch.ml.common.CommonValue.UNDEPLOYED;
import static org.opensearch.ml.common.MLModel.ALGORITHM_FIELD;
import static org.opensearch.ml.common.MLTask.ERROR_FIELD;
import static org.opensearch.ml.common.MLTask.FUNCTION_NAME_FIELD;
import static org.opensearch.ml.common.MLTask.MODEL_ID_FIELD;
import static org.opensearch.ml.common.MLTask.STATE_FIELD;
import static org.opensearch.ml.common.MLTaskState.COMPLETED;
import static org.opensearch.ml.common.MLTaskState.FAILED;
import static org.opensearch.ml.common.utils.StringUtils.getErrorMessage;
import static org.opensearch.ml.engine.ModelHelper.CHUNK_FILES;
import static org.opensearch.ml.engine.ModelHelper.CHUNK_SIZE;
import static org.opensearch.ml.engine.ModelHelper.MODEL_FILE_HASH;
import static org.opensearch.ml.engine.ModelHelper.MODEL_SIZE_IN_BYTES;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.CLIENT;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.CLUSTER_SERVICE;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.CONNECTOR_PRIVATE_IP_ENABLED;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.GUARDRAILS;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.RATE_LIMITER;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.SCRIPT_SERVICE;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.USER_RATE_LIMITER_MAP;
import static org.opensearch.ml.engine.algorithms.remote.RemoteModel.XCONTENT_REGISTRY;
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;
import static org.opensearch.ml.plugin.MachineLearningPlugin.REGISTER_THREAD_POOL;
import static org.opensearch.ml.settings.MLCommonsSettings.ML_COMMONS_MAX_BATCH_INFERENCE_TASKS;
import static org.opensearch.ml.settings.MLCommonsSettings.ML_COMMONS_MAX_BATCH_INGESTION_TASKS;
import static org.opensearch.ml.settings.MLCommonsSettings.ML_COMMONS_MAX_DEPLOY_MODEL_TASKS_PER_NODE;
import static org.opensearch.ml.settings.MLCommonsSettings.ML_COMMONS_MAX_MODELS_PER_NODE;
import static org.opensearch.ml.settings.MLCommonsSettings.ML_COMMONS_MAX_REGISTER_MODEL_TASKS_PER_NODE;
import static org.opensearch.ml.stats.ActionName.REGISTER;
import static org.opensearch.ml.stats.MLActionLevelStat.ML_ACTION_REQUEST_COUNT;
import static org.opensearch.ml.utils.MLExceptionUtils.CONTROLLER_DISABLED_ERR_MSG;
import static org.opensearch.ml.utils.MLExceptionUtils.logException;
import static org.opensearch.ml.utils.MLNodeUtils.checkOpenCircuitBreaker;
import static org.opensearch.ml.utils.MLNodeUtils.createXContentParserFromRegistry;
import java.io.File;
import java.io.IOException;
import java.nio.file.Path;
import java.security.PrivilegedActionException;
import java.time.Instant;
import java.util.Arrays;
import java.util.Base64;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.concurrent.ConcurrentLinkedDeque;
import java.util.concurrent.Semaphore;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.function.Supplier;
import java.util.stream.Collectors;
import org.apache.commons.lang3.BooleanUtils;
import org.apache.logging.log4j.util.Strings;
import org.opensearch.ExceptionsHelper;
import org.opensearch.OpenSearchStatusException;
import org.opensearch.action.DocWriteResponse;
import org.opensearch.action.delete.DeleteRequest;
import org.opensearch.action.get.GetRequest;
import org.opensearch.action.get.GetResponse;
import org.opensearch.action.index.IndexRequest;
import org.opensearch.action.index.IndexResponse;
import org.opensearch.action.support.IndicesOptions;
import org.opensearch.action.support.ThreadedActionListener;
import org.opensearch.action.support.WriteRequest;
import org.opensearch.action.update.UpdateRequest;
import org.opensearch.action.update.UpdateResponse;
import org.opensearch.client.Client;
import org.opensearch.cluster.node.DiscoveryNode;
import org.opensearch.cluster.service.ClusterService;
import org.opensearch.common.settings.Settings;
import org.opensearch.common.util.TokenBucket;
import org.opensearch.common.util.concurrent.ThreadContext;
import org.opensearch.common.xcontent.LoggingDeprecationHandler;
import org.opensearch.core.action.ActionListener;
import org.opensearch.core.rest.RestStatus;
import org.opensearch.core.xcontent.NamedXContentRegistry;
import org.opensearch.core.xcontent.XContentBuilder;
import org.opensearch.core.xcontent.XContentParser;
import org.opensearch.index.IndexNotFoundException;
import org.opensearch.index.query.TermQueryBuilder;
import org.opensearch.index.reindex.DeleteByQueryAction;
import org.opensearch.index.reindex.DeleteByQueryRequest;
import org.opensearch.ml.breaker.MLCircuitBreakerService;
import org.opensearch.ml.cluster.DiscoveryNodeHelper;
import org.opensearch.ml.common.FunctionName;
import org.opensearch.ml.common.MLModel;
import org.opensearch.ml.common.MLModelGroup;
import org.opensearch.ml.common.MLTask;
import org.opensearch.ml.common.MLTaskState;
import org.opensearch.ml.common.MLTaskType;
import org.opensearch.ml.common.connector.Connector;
import org.opensearch.ml.common.controller.MLController;
import org.opensearch.ml.common.controller.MLRateLimiter;
import org.opensearch.ml.common.exception.MLException;
import org.opensearch.ml.common.exception.MLLimitExceededException;
import org.opensearch.ml.common.exception.MLResourceNotFoundException;
import org.opensearch.ml.common.exception.MLValidationException;
import org.opensearch.ml.common.model.Guardrails;
import org.opensearch.ml.common.model.MLGuard;
import org.opensearch.ml.common.model.MLModelState;
import org.opensearch.ml.common.transport.deploy.MLDeployModelAction;
import org.opensearch.ml.common.transport.deploy.MLDeployModelRequest;
import org.opensearch.ml.common.transport.deploy.MLDeployModelResponse;
import org.opensearch.ml.common.transport.register.MLRegisterModelInput;
import org.opensearch.ml.common.transport.register.MLRegisterModelResponse;
import org.opensearch.ml.common.transport.upload_chunk.MLRegisterModelMetaInput;
import org.opensearch.ml.engine.MLEngine;
import org.opensearch.ml.engine.MLExecutable;
import org.opensearch.ml.engine.ModelHelper;
import org.opensearch.ml.engine.Predictable;
import org.opensearch.ml.engine.indices.MLIndicesHandler;
import org.opensearch.ml.engine.utils.FileUtils;
import org.opensearch.ml.profile.MLModelProfile;
import org.opensearch.ml.settings.MLFeatureEnabledSetting;
import org.opensearch.ml.stats.ActionName;
import org.opensearch.ml.stats.MLActionLevelStat;
import org.opensearch.ml.stats.MLNodeLevelStat;
import org.opensearch.ml.stats.MLStats;
import org.opensearch.ml.task.MLTaskManager;
import org.opensearch.ml.utils.MLExceptionUtils;
import org.opensearch.ml.utils.MLNodeUtils;
import org.opensearch.remote.metadata.client.GetDataObjectRequest;
import org.opensearch.remote.metadata.client.GetDataObjectResponse;
import org.opensearch.remote.metadata.client.PutDataObjectRequest;
import org.opensearch.remote.metadata.client.SdkClient;
import org.opensearch.remote.metadata.client.UpdateDataObjectRequest;
import org.opensearch.remote.metadata.client.UpdateDataObjectResponse;
import org.opensearch.remote.metadata.common.SdkClientUtils;
import org.opensearch.script.ScriptService;
import org.opensearch.search.fetch.subphase.FetchSourceContext;
import org.opensearch.threadpool.ThreadPool;
import com.google.common.annotations.VisibleForTesting;
import com.google.common.collect.ImmutableSet;
import com.google.common.io.Files;
import lombok.extern.log4j.Log4j2;
/**
* Manager class for ML models. It contains ML model related operations like
* register, deploy model etc.
*/
@Log4j2
public class MLModelManager {
public static final int TIMEOUT_IN_MILLIS = 5000;
public static final long MODEL_FILE_SIZE_LIMIT = 4L * 1024 * 1024 * 1024;// 4GB
private final Client client;
private final SdkClient sdkClient;
private final ClusterService clusterService;
private final ScriptService scriptService;
private final ThreadPool threadPool;
private final NamedXContentRegistry xContentRegistry;
private final ModelHelper modelHelper;
private final MLModelCacheHelper modelCacheHelper;
private final MLStats mlStats;
private final MLCircuitBreakerService mlCircuitBreakerService;
private final MLIndicesHandler mlIndicesHandler;
private final MLTaskManager mlTaskManager;
private final MLEngine mlEngine;
private final DiscoveryNodeHelper nodeHelper;
private final MLFeatureEnabledSetting mlFeatureEnabledSetting;
private volatile Integer maxModelPerNode;
private volatile Integer maxRegisterTasksPerNode;
private volatile Integer maxDeployTasksPerNode;
private volatile Integer maxBatchInferenceTasks;
private volatile Integer maxBatchIngestionTasks;
public static final ImmutableSet<MLModelState> MODEL_DONE_STATES = ImmutableSet
.of(
MLModelState.TRAINED,
MLModelState.REGISTERED,
MLModelState.DEPLOYED,
MLModelState.PARTIALLY_DEPLOYED,
MLModelState.DEPLOY_FAILED,
MLModelState.UNDEPLOYED
);
public MLModelManager(
ClusterService clusterService,
ScriptService scriptService,
Client client,
SdkClient sdkClient,
ThreadPool threadPool,
NamedXContentRegistry xContentRegistry,
ModelHelper modelHelper,
Settings settings,
MLStats mlStats,
MLCircuitBreakerService mlCircuitBreakerService,
MLIndicesHandler mlIndicesHandler,
MLTaskManager mlTaskManager,
MLModelCacheHelper modelCacheHelper,
MLEngine mlEngine,
DiscoveryNodeHelper nodeHelper,
MLFeatureEnabledSetting mlFeatureEnabledSetting
) {
this.client = client;
this.sdkClient = sdkClient;
this.threadPool = threadPool;
this.xContentRegistry = xContentRegistry;
this.modelHelper = modelHelper;
this.clusterService = clusterService;
this.scriptService = scriptService;
this.modelCacheHelper = modelCacheHelper;
this.mlStats = mlStats;
this.mlCircuitBreakerService = mlCircuitBreakerService;
this.mlIndicesHandler = mlIndicesHandler;
this.mlTaskManager = mlTaskManager;
this.mlEngine = mlEngine;
this.nodeHelper = nodeHelper;
this.mlFeatureEnabledSetting = mlFeatureEnabledSetting;
this.maxModelPerNode = ML_COMMONS_MAX_MODELS_PER_NODE.get(settings);
clusterService.getClusterSettings().addSettingsUpdateConsumer(ML_COMMONS_MAX_MODELS_PER_NODE, it -> maxModelPerNode = it);
maxRegisterTasksPerNode = ML_COMMONS_MAX_REGISTER_MODEL_TASKS_PER_NODE.get(settings);
clusterService
.getClusterSettings()
.addSettingsUpdateConsumer(ML_COMMONS_MAX_REGISTER_MODEL_TASKS_PER_NODE, it -> maxRegisterTasksPerNode = it);
maxDeployTasksPerNode = ML_COMMONS_MAX_DEPLOY_MODEL_TASKS_PER_NODE.get(settings);
clusterService
.getClusterSettings()
.addSettingsUpdateConsumer(ML_COMMONS_MAX_DEPLOY_MODEL_TASKS_PER_NODE, it -> maxDeployTasksPerNode = it);
maxBatchInferenceTasks = ML_COMMONS_MAX_BATCH_INFERENCE_TASKS.get(settings);
clusterService
.getClusterSettings()
.addSettingsUpdateConsumer(ML_COMMONS_MAX_BATCH_INFERENCE_TASKS, it -> maxBatchInferenceTasks = it);
maxBatchIngestionTasks = ML_COMMONS_MAX_BATCH_INGESTION_TASKS.get(settings);
clusterService
.getClusterSettings()
.addSettingsUpdateConsumer(ML_COMMONS_MAX_BATCH_INGESTION_TASKS, it -> maxBatchIngestionTasks = it);
}
public void registerModelMeta(MLRegisterModelMetaInput mlRegisterModelMetaInput, ActionListener<String> listener) {
try {
FunctionName functionName = mlRegisterModelMetaInput.getFunctionName();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(functionName, REGISTER, ML_ACTION_REQUEST_COUNT).increment();
String modelGroupId = mlRegisterModelMetaInput.getModelGroupId();
if (Strings.isBlank(modelGroupId)) {
uploadMLModelMeta(mlRegisterModelMetaInput, "1", listener);
} else {
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
ActionListener<String> wrappedListener = ActionListener.runBefore(listener, context::restore);
GetRequest getModelGroupRequest = new GetRequest(ML_MODEL_GROUP_INDEX).id(modelGroupId);
client.get(getModelGroupRequest, ActionListener.wrap(modelGroup -> {
if (modelGroup.isExists()) {
Map<String, Object> modelGroupSource = modelGroup.getSourceAsMap();
int updatedVersion = incrementLatestVersion(modelGroupSource);
UpdateRequest updateModelGroupRequest = createUpdateModelGroupRequest(
modelGroupSource,
modelGroupId,
modelGroup.getSeqNo(),
modelGroup.getPrimaryTerm(),
updatedVersion
);
client.update(updateModelGroupRequest, ActionListener.wrap(r -> {
uploadMLModelMeta(mlRegisterModelMetaInput, updatedVersion + "", wrappedListener);
}, e -> {
log.error("Failed to update model group", e);
wrappedListener.onFailure(e);
}));
} else {
log.error("Model group not found");
wrappedListener.onFailure(new MLResourceNotFoundException("Fail to find model group"));
}
}, e -> {
if (e instanceof IndexNotFoundException) {
wrappedListener.onFailure(new MLResourceNotFoundException("Fail to find model group"));
} else {
log.error("Failed to get model group", e);
wrappedListener.onFailure(new MLValidationException("Failed to get model group"));
}
}));
} catch (Exception e) {
log.error("Failed to register model", e);
listener.onFailure(e);
}
}
} catch (final Exception e) {
log.error("Failed to init model index", e);
listener.onFailure(e);
}
}
private void uploadMLModelMeta(MLRegisterModelMetaInput mlRegisterModelMetaInput, String version, ActionListener<String> listener) {
FunctionName functionName = mlRegisterModelMetaInput.getFunctionName();
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
ActionListener<String> wrappedListener = ActionListener.runBefore(listener, context::restore);
String modelName = mlRegisterModelMetaInput.getName();
mlIndicesHandler.initModelIndexIfAbsent(ActionListener.wrap(res -> {
if (!res) {
wrappedListener.onFailure(new RuntimeException("No response to create ML Model index"));
return;
}
Instant now = Instant.now();
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.algorithm(functionName)
.version(version)
.modelGroupId(mlRegisterModelMetaInput.getModelGroupId())
.description(mlRegisterModelMetaInput.getDescription())
.isEnabled(mlRegisterModelMetaInput.getIsEnabled())
.rateLimiter(mlRegisterModelMetaInput.getRateLimiter())
.modelFormat(mlRegisterModelMetaInput.getModelFormat())
.modelState(MLModelState.REGISTERING)
.modelConfig(mlRegisterModelMetaInput.getModelConfig())
.deploySetting(mlRegisterModelMetaInput.getDeploySetting())
.totalChunks(mlRegisterModelMetaInput.getTotalChunks())
.modelContentHash(mlRegisterModelMetaInput.getModelContentHashValue())
.modelContentSizeInBytes(mlRegisterModelMetaInput.getModelContentSizeInBytes())
.isHidden(mlRegisterModelMetaInput.getIsHidden())
.modelInterface(mlRegisterModelMetaInput.getModelInterface())
.createdTime(now)
.lastUpdateTime(now)
.build();
IndexRequest indexRequest = new IndexRequest(ML_MODEL_INDEX);
if (mlRegisterModelMetaInput.getIsHidden() != null && mlRegisterModelMetaInput.getIsHidden()) {
indexRequest.id(modelName);
}
indexRequest.source(mlModelMeta.toXContent(XContentBuilder.builder(JSON.xContent()), EMPTY_PARAMS));
indexRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
client.index(indexRequest, ActionListener.wrap(response -> {
log.debug("Index model meta doc successfully {}", modelName);
wrappedListener.onResponse(response.getId());
}, e -> {
deleteOrUpdateModelGroup(
mlRegisterModelMetaInput.getModelGroupId(),
mlRegisterModelMetaInput.getDoesVersionCreateModelGroup(),
version
);
log.error("Failed to index model meta doc", e);
wrappedListener.onFailure(e);
}));
}, ex -> {
log.error("Failed to init model index", ex);
wrappedListener.onFailure(ex);
}));
} catch (Exception e) {
log.error("Failed to register model", e);
listener.onFailure(e);
}
}
/**
* @param sdkClient metadata client
* @param mlRegisterModelInput register model input for remote models
* @param mlTask ML task
* @param listener action listener
*/
public void registerMLRemoteModel(
SdkClient sdkClient,
MLRegisterModelInput mlRegisterModelInput,
MLTask mlTask,
ActionListener<MLRegisterModelResponse> listener
) {
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
checkAndAddRunningTask(mlTask, maxRegisterTasksPerNode);
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(mlTask.getFunctionName(), REGISTER, ML_ACTION_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
String modelGroupId = mlRegisterModelInput.getModelGroupId();
GetDataObjectRequest getModelGroupRequest = GetDataObjectRequest
.builder()
.index(ML_MODEL_GROUP_INDEX)
.tenantId(mlRegisterModelInput.getTenantId())
.id(modelGroupId)
.build();
sdkClient.getDataObjectAsync(getModelGroupRequest).whenComplete((r, throwable) -> {
if (throwable == null) {
try {
GetResponse getModelGroupResponse = GetResponse.fromXContent(r.parser());
if (getModelGroupResponse.isExists()) {
Map<String, Object> modelGroupSourceMap = getModelGroupResponse.getSourceAsMap();
int updatedVersion = incrementLatestVersion(modelGroupSourceMap);
modelGroupSourceMap.put(MLModelGroup.LATEST_VERSION_FIELD, updatedVersion);
modelGroupSourceMap.put(MLModelGroup.LAST_UPDATED_TIME_FIELD, Instant.now().toEpochMilli());
UpdateDataObjectRequest updateDataObjectRequest = UpdateDataObjectRequest
.builder()
.index(ML_MODEL_GROUP_INDEX)
.id(modelGroupId)
.tenantId(mlRegisterModelInput.getTenantId())
.ifSeqNo(getModelGroupResponse.getSeqNo())
.ifPrimaryTerm(getModelGroupResponse.getPrimaryTerm())
.dataObject(modelGroupSourceMap)
.build();
try (ThreadContext.StoredContext innerContext = client.threadPool().getThreadContext().stashContext()) {
sdkClient.updateDataObjectAsync(updateDataObjectRequest).whenComplete((ur, ut) -> {
if (ut == null) {
indexRemoteModel(sdkClient, mlRegisterModelInput, mlTask, updatedVersion + "", listener);
} else {
Exception e = SdkClientUtils.unwrapAndConvertToException(ut);
log.error("Failed to update model group {}", modelGroupId, e);
handleException(
mlRegisterModelInput.getFunctionName(),
mlTask.getTaskId(),
mlRegisterModelInput.getTenantId(),
e
);
listener.onFailure(e);
}
});
}
} else {
log.error("Model group response is empty");
handleException(
mlRegisterModelInput.getFunctionName(),
mlTask.getTaskId(),
mlRegisterModelInput.getTenantId(),
new MLValidationException("Model group not found")
);
listener.onFailure(new MLResourceNotFoundException("Model Group Response is empty for " + modelGroupId));
}
} catch (Exception e) {
listener.onFailure(e);
}
} else {
Exception e = SdkClientUtils.unwrapAndConvertToException(throwable);
if (ExceptionsHelper.unwrap(e, IndexNotFoundException.class) != null) {
log.error("Model group Index is missing");
handleException(
mlRegisterModelInput.getFunctionName(),
mlTask.getTaskId(),
mlRegisterModelInput.getTenantId(),
new MLResourceNotFoundException("Failed to get model group due to index missing")
);
listener.onFailure(e);
} else {
log.error("Failed to get model group", e);
handleException(mlRegisterModelInput.getFunctionName(), mlTask.getTaskId(), mlRegisterModelInput.getTenantId(), e);
listener.onFailure(e);
}
}
});
} catch (Exception e) {
log.error("Failed to register remote model", e);
handleException(mlRegisterModelInput.getFunctionName(), mlTask.getTaskId(), mlRegisterModelInput.getTenantId(), e);
listener.onFailure(e);
} finally {
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).decrement();
}
}
/**
* Register model. Basically download model file, split into chunks and save
* into model index.
*
* @param registerModelInput register model input
* @param mlTask ML task
*/
public void registerMLModel(MLRegisterModelInput registerModelInput, MLTask mlTask) {
checkAndAddRunningTask(mlTask, maxRegisterTasksPerNode);
try {
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(mlTask.getFunctionName(), REGISTER, ML_ACTION_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
String modelGroupId = registerModelInput.getModelGroupId();
GetRequest getModelGroupRequest = new GetRequest(ML_MODEL_GROUP_INDEX).id(modelGroupId);
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
client.get(getModelGroupRequest, ActionListener.runBefore(ActionListener.wrap(modelGroup -> {
if (modelGroup.isExists()) {
Map<String, Object> modelGroupSourceMap = modelGroup.getSourceAsMap();
int updatedVersion = incrementLatestVersion(modelGroupSourceMap);
UpdateRequest updateModelGroupRequest = createUpdateModelGroupRequest(
modelGroupSourceMap,
modelGroupId,
modelGroup.getSeqNo(),
modelGroup.getPrimaryTerm(),
updatedVersion
);
try (ThreadContext.StoredContext threadContext = client.threadPool().getThreadContext().stashContext()) {
client
.update(
updateModelGroupRequest,
ActionListener.wrap(r -> { uploadModel(registerModelInput, mlTask, updatedVersion + ""); }, e -> {
log.error("Failed to update model group", e);
handleException(
registerModelInput.getFunctionName(),
mlTask.getTaskId(),
registerModelInput.getTenantId(),
e
);
})
);
}
} else {
log.error("Model group not found");
handleException(
registerModelInput.getFunctionName(),
mlTask.getTaskId(),
registerModelInput.getTenantId(),
new MLValidationException("Model group not found")
);
}
}, e -> {
if (e instanceof IndexNotFoundException) {
handleException(
registerModelInput.getFunctionName(),
mlTask.getTaskId(),
registerModelInput.getTenantId(),
new MLResourceNotFoundException("Failed to get model group")
);
} else {
log.error("Failed to get model group", e);
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), registerModelInput.getTenantId(), e);
}
}), context::restore));
} catch (Exception e) {
log.error("Failed to register model", e);
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), registerModelInput.getTenantId(), e);
}
} catch (Exception e) {
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), registerModelInput.getTenantId(), e);
} finally {
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).decrement();
}
}
private UpdateRequest createUpdateModelGroupRequest(
Map<String, Object> modelGroupSourceMap,
String modelGroupId,
long seqNo,
long primaryTerm,
int updatedVersion
) {
modelGroupSourceMap.put(MLModelGroup.LATEST_VERSION_FIELD, updatedVersion);
modelGroupSourceMap.put(MLModelGroup.LAST_UPDATED_TIME_FIELD, Instant.now().toEpochMilli());
UpdateRequest updateModelGroupRequest = new UpdateRequest();
updateModelGroupRequest
.index(ML_MODEL_GROUP_INDEX)
.id(modelGroupId)
.setIfSeqNo(seqNo)
.setIfPrimaryTerm(primaryTerm)
.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE)
.doc(modelGroupSourceMap);
return updateModelGroupRequest;
}
private int incrementLatestVersion(Map<String, Object> modelGroupSourceMap) {
return Integer.parseInt(modelGroupSourceMap.get(MLModelGroup.LATEST_VERSION_FIELD).toString()) + 1;
}
private void indexRemoteModel(
SdkClient sdkClient,
MLRegisterModelInput registerModelInput,
MLTask mlTask,
String modelVersion,
ActionListener<MLRegisterModelResponse> listener
) {
String taskId = mlTask.getTaskId();
FunctionName functionName = mlTask.getFunctionName();
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
String modelName = registerModelInput.getModelName();
String version = modelVersion == null ? registerModelInput.getVersion() : modelVersion;
Instant now = Instant.now();
if (registerModelInput.getConnector() != null) {
registerModelInput.getConnector().encrypt(mlEngine::encrypt, registerModelInput.getTenantId());
}
mlIndicesHandler.initModelIndexIfAbsent(ActionListener.wrap(boolResponse -> {
if (!boolResponse) {
listener.onFailure(new RuntimeException("No response to create ML Model index"));
return;
}
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.algorithm(functionName)
.modelGroupId(registerModelInput.getModelGroupId())
.version(version)
.description(registerModelInput.getDescription())
.rateLimiter(registerModelInput.getRateLimiter())
.modelFormat(registerModelInput.getModelFormat())
.modelState(MLModelState.REGISTERED)
.connector(registerModelInput.getConnector())
.connectorId(registerModelInput.getConnectorId())
.modelConfig(registerModelInput.getModelConfig())
.deploySetting(registerModelInput.getDeploySetting())
.createdTime(now)
.lastUpdateTime(now)
.isHidden(registerModelInput.getIsHidden())
.guardrails(registerModelInput.getGuardrails())
.modelInterface(registerModelInput.getModelInterface())
.tenantId(registerModelInput.getTenantId())
.build();
PutDataObjectRequest putModelMetaRequest = PutDataObjectRequest
.builder()
.index(ML_MODEL_INDEX)
.id(Boolean.TRUE.equals(registerModelInput.getIsHidden()) ? modelName : null)
.tenantId(registerModelInput.getTenantId())
.dataObject(mlModelMeta)
.build();
// index remote model doc
ActionListener<IndexResponse> indexListener = ActionListener.wrap(modelMetaRes -> {
String modelId = modelMetaRes.getId();
mlTask.setModelId(modelId);
log.info("create new model meta doc {} for upload task {}", modelId, taskId);
mlTaskManager
.updateMLTask(
taskId,
registerModelInput.getTenantId(),
Map.of(MODEL_ID_FIELD, modelId, STATE_FIELD, COMPLETED),
5000,
true
);
if (registerModelInput.isDeployModel()) {
deployModelAfterRegistering(registerModelInput, modelId);
}
listener.onResponse(new MLRegisterModelResponse(taskId, MLTaskState.CREATED.name(), modelId));
}, e -> {
log.error("Failed to index model meta doc", e);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
listener.onFailure(e);
});
ThreadedActionListener<IndexResponse> putListener = threadedActionListener(REGISTER_THREAD_POOL, indexListener);
sdkClient.putDataObjectAsync(putModelMetaRequest).whenComplete((r, throwable) -> {
if (throwable == null) {
try {
IndexResponse ir = IndexResponse.fromXContent(r.parser());
putListener.onResponse(ir);
} catch (Exception e) {
putListener.onFailure(e);
}
} else {
Exception e = SdkClientUtils.unwrapAndConvertToException(throwable);
putListener.onFailure(e);
}
});
}, error -> {
// failed to initialize the model index
log.error("Failed to init model index", error);
handleException(functionName, taskId, registerModelInput.getTenantId(), error);
listener.onFailure(error);
}));
}
}
@VisibleForTesting
void indexRemoteModel(MLRegisterModelInput registerModelInput, MLTask mlTask, String modelVersion) {
String taskId = mlTask.getTaskId();
FunctionName functionName = mlTask.getFunctionName();
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
String modelName = registerModelInput.getModelName();
String version = modelVersion == null ? registerModelInput.getVersion() : modelVersion;
Instant now = Instant.now();
if (registerModelInput.getConnector() != null) {
registerModelInput.getConnector().encrypt(mlEngine::encrypt, registerModelInput.getTenantId());
}
mlIndicesHandler.initModelIndexIfAbsent(ActionListener.runBefore(ActionListener.wrap(res -> {
if (!res) {
handleException(
functionName,
taskId,
registerModelInput.getTenantId(),
new RuntimeException("No response to create ML Model index")
);
return;
}
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.algorithm(functionName)
.modelGroupId(registerModelInput.getModelGroupId())
.version(version)
.description(registerModelInput.getDescription())
.rateLimiter(registerModelInput.getRateLimiter())
.modelFormat(registerModelInput.getModelFormat())
.modelState(MLModelState.REGISTERED)
.connector(registerModelInput.getConnector())
.connectorId(registerModelInput.getConnectorId())
.modelConfig(registerModelInput.getModelConfig())
.deploySetting(registerModelInput.getDeploySetting())
.createdTime(now)
.lastUpdateTime(now)
.isHidden(registerModelInput.getIsHidden())
.guardrails(registerModelInput.getGuardrails())
.modelInterface(registerModelInput.getModelInterface())
.tenantId(registerModelInput.getTenantId())
.build();
PutDataObjectRequest putModelMetaRequest = PutDataObjectRequest
.builder()
.index(ML_MODEL_INDEX)
.id(Boolean.TRUE.equals(registerModelInput.getIsHidden()) ? modelName : null)
.tenantId(registerModelInput.getTenantId())
.dataObject(mlModelMeta)
.build();
IndexRequest indexModelMetaRequest = new IndexRequest(ML_MODEL_INDEX);
if (registerModelInput.getIsHidden() != null && registerModelInput.getIsHidden()) {
indexModelMetaRequest.id(modelName);
}
indexModelMetaRequest.source(mlModelMeta.toXContent(XContentBuilder.builder(JSON.xContent()), EMPTY_PARAMS));
indexModelMetaRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
// create model meta doc
ActionListener<IndexResponse> indexListener = ActionListener.wrap(modelMetaRes -> {
String modelId = modelMetaRes.getId();
mlTask.setModelId(modelId);
log.info("create new model meta doc {} for upload task {}", modelId, taskId);
mlTaskManager
.updateMLTask(
taskId,
registerModelInput.getTenantId(),
Map.of(MODEL_ID_FIELD, modelId, STATE_FIELD, COMPLETED),
5000,
true
);
if (registerModelInput.isDeployModel()) {
deployModelAfterRegistering(registerModelInput, modelId);
}
}, e -> {
log.error("Failed to index model meta doc", e);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
});
ThreadedActionListener<IndexResponse> putListener = threadedActionListener(REGISTER_THREAD_POOL, indexListener);
sdkClient.putDataObjectAsync(putModelMetaRequest).whenComplete((r, throwable) -> {
if (throwable == null) {
try {
IndexResponse ir = IndexResponse.fromXContent(r.parser());
putListener.onResponse(ir);
} catch (Exception e) {
putListener.onFailure(e);
}
} else {
Exception e = SdkClientUtils.unwrapAndConvertToException(throwable);
putListener.onFailure(e);
}
});
}, e -> {
log.error("Failed to init model index", e);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
}), context::restore));
} catch (Exception e) {
logException("Failed to upload model", e, log);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
}
}
private void uploadModel(MLRegisterModelInput registerModelInput, MLTask mlTask, String modelVersion) throws PrivilegedActionException {
if (registerModelInput.getUrl() != null) {
registerModelFromUrl(registerModelInput, mlTask, modelVersion);
} else if (registerModelInput.getFunctionName() == FunctionName.REMOTE || registerModelInput.getConnectorId() != null) {
indexRemoteModel(registerModelInput, mlTask, modelVersion);
} else {
registerPrebuiltModel(registerModelInput, mlTask, modelVersion);
}
}
private void registerModelFromUrl(MLRegisterModelInput registerModelInput, MLTask mlTask, String modelVersion) {
String taskId = mlTask.getTaskId();
FunctionName functionName = mlTask.getFunctionName();
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
String modelName = registerModelInput.getModelName();
String version = modelVersion == null ? registerModelInput.getVersion() : modelVersion;
String modelGroupId = registerModelInput.getModelGroupId();
Instant now = Instant.now();
mlIndicesHandler.initModelIndexIfAbsent(ActionListener.runBefore(ActionListener.wrap(res -> {
if (!res) {
handleException(
functionName,
taskId,
registerModelInput.getTenantId(),
new RuntimeException("No response to create ML Model index")
);
return;
}
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.modelGroupId(modelGroupId)
.algorithm(functionName)
.version(version)
.description(registerModelInput.getDescription())
.rateLimiter(registerModelInput.getRateLimiter())
.modelFormat(registerModelInput.getModelFormat())
.modelState(MLModelState.REGISTERING)
.modelConfig(registerModelInput.getModelConfig())
.deploySetting(registerModelInput.getDeploySetting())
.createdTime(now)
.lastUpdateTime(now)
.isHidden(registerModelInput.getIsHidden())
.guardrails(registerModelInput.getGuardrails())
.modelInterface(registerModelInput.getModelInterface())
.build();
IndexRequest indexModelMetaRequest = new IndexRequest(ML_MODEL_INDEX);
if (functionName == FunctionName.METRICS_CORRELATION) {
indexModelMetaRequest.id(functionName.name());
}
if (registerModelInput.getIsHidden() != null && registerModelInput.getIsHidden()) {
indexModelMetaRequest.id(modelName);
}
indexModelMetaRequest.source(mlModelMeta.toXContent(XContentBuilder.builder(JSON.xContent()), EMPTY_PARAMS));
indexModelMetaRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
// create model meta doc
ActionListener<IndexResponse> listener = ActionListener.wrap(modelMetaRes -> {
String modelId = modelMetaRes.getId();
mlTask.setModelId(modelId);
log.info("create new model meta doc {} for register model task {}", modelId, taskId);
// model group id is not present in request body.
registerModel(registerModelInput, taskId, functionName, modelName, version, modelId);
}, e -> {
log.error("Failed to index model meta doc", e);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
});
client.index(indexModelMetaRequest, threadedActionListener(REGISTER_THREAD_POOL, listener));
}, e -> {
log.error("Failed to init model index", e);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
}), context::restore));
} catch (Exception e) {
logException("Failed to register model", e, log);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
}
}
private void registerModel(
MLRegisterModelInput registerModelInput,
String taskId,
FunctionName functionName,
String modelName,
String version,
String modelId
) {
modelHelper
.downloadAndSplit(
registerModelInput.getModelFormat(),
modelId,
modelName,
version,
registerModelInput.getUrl(),
registerModelInput.getHashValue(),
functionName,
ActionListener.wrap(result -> {
Long modelSizeInBytes = (Long) result.get(MODEL_SIZE_IN_BYTES);
if (modelSizeInBytes >= MODEL_FILE_SIZE_LIMIT) {
throw new MLException("Model file size exceeds the limit of 4GB: " + modelSizeInBytes);
}
List<String> chunkFiles = (List<String>) result.get(CHUNK_FILES);
String hashValue = (String) result.get(MODEL_FILE_HASH);
Semaphore semaphore = new Semaphore(1);
AtomicInteger uploaded = new AtomicInteger(0);
AtomicBoolean failedToUploadChunk = new AtomicBoolean(false);
// upload chunks
for (String name : chunkFiles) {
semaphore.tryAcquire(10, TimeUnit.SECONDS);
if (failedToUploadChunk.get()) {
throw new MLException("Failed to save model chunk");
}
File file = new File(name);
byte[] bytes = Files.toByteArray(file);
int chunkNum = Integer.parseInt(file.getName());
Instant now = Instant.now();
MLModel mlModel = MLModel
.builder()
.modelId(modelId)
.name(modelName)
.algorithm(functionName)
.version(version)
.modelFormat(registerModelInput.getModelFormat())
.rateLimiter(registerModelInput.getRateLimiter())
.isEnabled(registerModelInput.getIsEnabled())
.chunkNumber(chunkNum)
.totalChunks(chunkFiles.size())
.content(Base64.getEncoder().encodeToString(bytes))
.createdTime(now)
.lastUpdateTime(now)
.isHidden(registerModelInput.getIsHidden())
.guardrails(registerModelInput.getGuardrails())
.modelInterface(registerModelInput.getModelInterface())
.build();
IndexRequest indexRequest = new IndexRequest(ML_MODEL_INDEX);
if (registerModelInput.getIsHidden() != null && registerModelInput.getIsHidden()) {
indexRequest.id(modelName);
}
String chunkId = getModelChunkId(modelId, chunkNum);
indexRequest.id(chunkId);
indexRequest.source(mlModel.toXContent(XContentBuilder.builder(JSON.xContent()), EMPTY_PARAMS));
indexRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
client.index(indexRequest, ActionListener.wrap(r -> {
uploaded.getAndIncrement();
if (uploaded.get() == chunkFiles.size()) {
updateModelRegisterStateAsDone(
registerModelInput,
taskId,
modelId,
modelSizeInBytes,
chunkFiles,
hashValue,
version
);
} else {
deleteFileQuietly(file);
}
semaphore.release();
}, e -> {
log.error("Failed to index model chunk {}", chunkId, e);
failedToUploadChunk.set(true);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
deleteFileQuietly(file);
// remove model doc as failed to upload model
deleteModel(modelId, registerModelInput, version);
semaphore.release();
deleteFileQuietly(mlEngine.getRegisterModelPath(modelId));
}));
}
}, e -> {
log.error("Failed to index chunk file", e);
deleteFileQuietly(mlEngine.getRegisterModelPath(modelId));
deleteModel(modelId, registerModelInput, version);
handleException(functionName, taskId, registerModelInput.getTenantId(), e);
})
);
}
private void registerPrebuiltModel(MLRegisterModelInput registerModelInput, MLTask mlTask, String modelVersion)
throws PrivilegedActionException {
String taskId = mlTask.getTaskId();
List modelMetaList = modelHelper.downloadPrebuiltModelMetaList(taskId, registerModelInput);
if (!modelHelper.isModelAllowed(registerModelInput, modelMetaList)) {
throw new IllegalArgumentException("This model is not in the pre-trained model list, please check your parameters.");
}
modelHelper.downloadPrebuiltModelConfig(taskId, registerModelInput, ActionListener.wrap(mlRegisterModelInput -> {
mlTask.setFunctionName(mlRegisterModelInput.getFunctionName());
mlTaskManager
.updateMLTask(
taskId,
registerModelInput.getTenantId(),
Map.of(FUNCTION_NAME_FIELD, mlRegisterModelInput.getFunctionName()),
TIMEOUT_IN_MILLIS,
false
);
registerModelFromUrl(mlRegisterModelInput, mlTask, modelVersion);
}, e -> {
log.error("Failed to register prebuilt model", e);
handleException(registerModelInput.getFunctionName(), taskId, registerModelInput.getTenantId(), e);
}));
}
private <T> ThreadedActionListener<T> threadedActionListener(String threadPoolName, ActionListener<T> listener) {
return new ThreadedActionListener<>(log, threadPool, threadPoolName, listener, false);
}
/**
* Check if exceed running task limit and if circuit breaker is open.
*
* @param mlTask ML task
* @param runningTaskLimit limit
*/
public void checkAndAddRunningTask(MLTask mlTask, Integer runningTaskLimit) {
if (Objects.nonNull(mlTask) && mlTask.getFunctionName() != FunctionName.REMOTE) {