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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.core.xcontent.ToXContent.EMPTY_PARAMS;
import static org.opensearch.core.xcontent.XContentParserUtils.ensureExpectedToken;
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.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_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.logException;
import static org.opensearch.ml.utils.MLNodeUtils.checkOpenCircuitBreaker;
import static org.opensearch.ml.utils.MLNodeUtils.createXContentParserFromRegistry;
import java.io.File;
import java.nio.file.Path;
import java.security.PrivilegedActionException;
import java.time.Instant;
import java.util.ArrayList;
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.OpenSearchStatusException;
import org.opensearch.action.delete.DeleteRequest;
import org.opensearch.action.get.GetRequest;
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.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.CommonValue;
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.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.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.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 ClusterService clusterService;
private final ScriptService scriptService;
private ThreadPool threadPool;
private NamedXContentRegistry xContentRegistry;
private 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 volatile Integer maxModelPerNode;
private volatile Integer maxRegisterTasksPerNode;
private volatile Integer maxDeployTasksPerNode;
public static final ImmutableSet 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,
ThreadPool threadPool,
NamedXContentRegistry xContentRegistry,
ModelHelper modelHelper,
Settings settings,
MLStats mlStats,
MLCircuitBreakerService mlCircuitBreakerService,
MLIndicesHandler mlIndicesHandler,
MLTaskManager mlTaskManager,
MLModelCacheHelper modelCacheHelper,
MLEngine mlEngine,
DiscoveryNodeHelper nodeHelper
) {
this.client = client;
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.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);
}
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 -> {
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())
.isEnabled(mlRegisterModelMetaInput.getIsEnabled())
.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 mlRegisterModelInput register model input for remote models
* @param mlTask ML task
* @param listener action listener
*/
public void registerMLRemoteModel(
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();
GetRequest getModelGroupRequest = new GetRequest(ML_MODEL_GROUP_INDEX).id(modelGroupId);
client.get(getModelGroupRequest, ActionListener.wrap(getModelGroupResponse -> {
if (getModelGroupResponse.isExists()) {
Map<String, Object> modelGroupSourceMap = getModelGroupResponse.getSourceAsMap();
int updatedVersion = incrementLatestVersion(modelGroupSourceMap);
UpdateRequest updateModelGroupRequest = createUpdateModelGroupRequest(
modelGroupSourceMap,
modelGroupId,
getModelGroupResponse.getSeqNo(),
getModelGroupResponse.getPrimaryTerm(),
updatedVersion
);
client.update(updateModelGroupRequest, ActionListener.wrap(r -> {
indexRemoteModel(mlRegisterModelInput, mlTask, updatedVersion + "", listener);
}, e -> {
log.error("Failed to update model group " + modelGroupId, e);
handleException(mlRegisterModelInput.getFunctionName(), mlTask.getTaskId(), e);
listener.onFailure(e);
}));
} else {
log.error("Model group response is empty");
handleException(
mlRegisterModelInput.getFunctionName(),
mlTask.getTaskId(),
new MLValidationException("Model group not found")
);
listener.onFailure(new MLResourceNotFoundException("Model Group Response is empty for " + modelGroupId));
}
}, error -> {
if (error instanceof IndexNotFoundException) {
log.error("Model group Index is missing");
handleException(
mlRegisterModelInput.getFunctionName(),
mlTask.getTaskId(),
new MLResourceNotFoundException("Failed to get model group due to index missing")
);
listener.onFailure(error);
} else {
log.error("Failed to get model group", error);
handleException(mlRegisterModelInput.getFunctionName(), mlTask.getTaskId(), error);
listener.onFailure(error);
}
}));
} catch (Exception e) {
log.error("Failed to register remote model", e);
handleException(mlRegisterModelInput.getFunctionName(), mlTask.getTaskId(), 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(), e);
})
);
}
} else {
log.error("Model group not found");
handleException(
registerModelInput.getFunctionName(),
mlTask.getTaskId(),
new MLValidationException("Model group not found")
);
}
}, e -> {
if (e instanceof IndexNotFoundException) {
handleException(
registerModelInput.getFunctionName(),
mlTask.getTaskId(),
new MLResourceNotFoundException("Failed to get model group")
);
} else {
log.error("Failed to get model group", e);
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), e);
}
}), () -> context.restore()));
} catch (Exception e) {
log.error("Failed to register model", e);
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), e);
}
} catch (Exception e) {
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), 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 (int) modelGroupSourceMap.get(MLModelGroup.LATEST_VERSION_FIELD) + 1;
}
private void indexRemoteModel(
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);
}
mlIndicesHandler.initModelIndexIfAbsent(ActionListener.wrap(boolResponse -> {
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.algorithm(functionName)
.modelGroupId(registerModelInput.getModelGroupId())
.version(version)
.description(registerModelInput.getDescription())
.rateLimiter(registerModelInput.getRateLimiter())
.isEnabled(registerModelInput.getIsEnabled())
.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())
.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);
// 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, 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, e);
listener.onFailure(e);
});
client.index(indexModelMetaRequest, threadedActionListener(REGISTER_THREAD_POOL, indexListener));
}, error -> {
// failed to initialize the model index
log.error("Failed to init model index", error);
handleException(functionName, taskId, 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);
}
mlIndicesHandler.initModelIndexIfAbsent(ActionListener.runBefore(ActionListener.wrap(res -> {
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.algorithm(functionName)
.modelGroupId(registerModelInput.getModelGroupId())
.version(version)
.description(registerModelInput.getDescription())
.rateLimiter(registerModelInput.getRateLimiter())
.isEnabled(registerModelInput.getIsEnabled())
.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())
.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, 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, e);
});
client.index(indexModelMetaRequest, threadedActionListener(REGISTER_THREAD_POOL, indexListener));
}, e -> {
log.error("Failed to init model index", e);
handleException(functionName, taskId, e);
}), () -> context.restore()));
} catch (Exception e) {
logException("Failed to upload model", e, log);
handleException(functionName, taskId, 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 -> {
MLModel mlModelMeta = MLModel
.builder()
.name(modelName)
.modelGroupId(modelGroupId)
.algorithm(functionName)
.version(version)
.description(registerModelInput.getDescription())
.rateLimiter(registerModelInput.getRateLimiter())
.isEnabled(registerModelInput.getIsEnabled())
.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, e);
});
client.index(indexModelMetaRequest, threadedActionListener(REGISTER_THREAD_POOL, listener));
}, e -> {
log.error("Failed to init model index", e);
handleException(functionName, taskId, e);
}), () -> context.restore()));
} catch (Exception e) {
logException("Failed to register model", e, log);
handleException(functionName, taskId, 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, 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, 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, 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, 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) {
checkOpenCircuitBreaker(mlCircuitBreakerService, mlStats);
}
mlTaskManager.checkLimitAndAddRunningTask(mlTask, runningTaskLimit);
}
private void updateModelRegisterStateAsDone(
MLRegisterModelInput registerModelInput,
String taskId,
String modelId,
Long modelSizeInBytes,
List<String> chunkFiles,
String hashValue,
String version
) {
FunctionName functionName = registerModelInput.getFunctionName();
deleteFileQuietly(mlEngine.getRegisterModelPath(modelId));
Map<String, Object> updatedFields = Map
.of(
MLModel.MODEL_STATE_FIELD,
MLModelState.REGISTERED,
MLModel.LAST_REGISTERED_TIME_FIELD,
Instant.now().toEpochMilli(),
MLModel.TOTAL_CHUNKS_FIELD,
chunkFiles.size(),
MLModel.MODEL_CONTENT_HASH_VALUE_FIELD,
hashValue,
MLModel.MODEL_CONTENT_SIZE_IN_BYTES_FIELD,
modelSizeInBytes
);
log.info("Model registered successfully, model id: {}, task id: {}", modelId, taskId);
updateModel(modelId, updatedFields, ActionListener.wrap(updateResponse -> {
mlTaskManager.updateMLTask(taskId, Map.of(STATE_FIELD, COMPLETED, MODEL_ID_FIELD, modelId), TIMEOUT_IN_MILLIS, true);
if (registerModelInput.isDeployModel()) {
deployModelAfterRegistering(registerModelInput, modelId);
}
}, e -> {
log.error("Failed to update model", e);
handleException(functionName, taskId, e);
deleteModel(modelId, registerModelInput, version);
}));
}
@VisibleForTesting
void deployModelAfterRegistering(MLRegisterModelInput registerModelInput, String modelId) {
String[] modelNodeIds = registerModelInput.getModelNodeIds();
log.debug("start deploying model after registering, modelId: {} on nodes: {}", modelId, Arrays.toString(modelNodeIds));
MLDeployModelRequest request = new MLDeployModelRequest(modelId, modelNodeIds, false, true, true);
ActionListener<MLDeployModelResponse> listener = ActionListener
.wrap(r -> log.debug("model deployed, response {}", r), e -> log.error("Failed to deploy model", e));
client.execute(MLDeployModelAction.INSTANCE, request, listener);
}
private void deleteModel(String modelId, MLRegisterModelInput registerModelInput, String modelVersion) {
DeleteRequest deleteRequest = new DeleteRequest();
deleteRequest.index(ML_MODEL_INDEX).id(modelId).setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
client.delete(deleteRequest);
DeleteByQueryRequest deleteChunksRequest = new DeleteByQueryRequest(ML_MODEL_INDEX)
.setQuery(new TermQueryBuilder(MLModel.MODEL_ID_FIELD, modelId))
.setIndicesOptions(IndicesOptions.LENIENT_EXPAND_OPEN)
.setAbortOnVersionConflict(false);
client.execute(DeleteByQueryAction.INSTANCE, deleteChunksRequest);
deleteOrUpdateModelGroup(registerModelInput.getModelGroupId(), registerModelInput.getDoesVersionCreateModelGroup(), modelVersion);
}
private void deleteOrUpdateModelGroup(String modelGroupID, Boolean doesVersionCreateModelGroup, String modelVersion) {
// This checks if model group is created when registering the version. If yes,
// model group is deleted since the version registration
// had failed. Else model group latest version is decremented by 1
if (doesVersionCreateModelGroup) {
DeleteRequest deleteModelGroupRequest = new DeleteRequest();
deleteModelGroupRequest.index(ML_MODEL_GROUP_INDEX).id(modelGroupID).setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
client.delete(deleteModelGroupRequest);
} else {
updateLatestVersionInModelGroup(
modelGroupID,
Integer.parseInt(modelVersion) - 1,
ActionListener
.wrap(r -> log.debug("model group updated, response {}", r), e -> log.error("Failed to update model group", e))
);
}
}
private void updateLatestVersionInModelGroup(String modelGroupID, Integer latestVersion, ActionListener<UpdateResponse> listener) {
Map<String, Object> updatedFields = new HashMap<>();
updatedFields.put(MLModelGroup.LATEST_VERSION_FIELD, latestVersion);
updatedFields.put(MLModelGroup.LAST_UPDATED_TIME_FIELD, Instant.now().toEpochMilli());
UpdateRequest updateRequest = new UpdateRequest(ML_MODEL_GROUP_INDEX, modelGroupID);
updateRequest.doc(updatedFields);
updateRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
client.update(updateRequest, ActionListener.runBefore(listener, () -> context.restore()));
} catch (Exception e) {
listener.onFailure(e);
}
}
private void handleException(FunctionName functionName, String taskId, Exception e) {
if (!(e instanceof MLLimitExceededException)
&& !(e instanceof MLResourceNotFoundException)
&& !(e instanceof IllegalArgumentException)) {
mlStats.createCounterStatIfAbsent(functionName, REGISTER, MLActionLevelStat.ML_ACTION_FAILURE_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_FAILURE_COUNT).increment();
}
Map<String, Object> updated = Map.of(ERROR_FIELD, MLExceptionUtils.getRootCauseMessage(e), STATE_FIELD, FAILED);
mlTaskManager.updateMLTask(taskId, updated, TIMEOUT_IN_MILLIS, true);
}
/**
* Read model chunks from model index. Concat chunks into a whole model file,
* then load
* into memory.
*
* @param modelId model id
* @param modelContentHash model content hash value
* @param functionName function name
* @param mlTask ML task
* @param listener action listener
*/
public void deployModel(
String modelId,
String modelContentHash,
FunctionName functionName,
boolean deployToAllNodes,
boolean autoDeployModel,
MLTask mlTask,
ActionListener<String> listener
) {
mlStats.createCounterStatIfAbsent(functionName, ActionName.DEPLOY, ML_ACTION_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats.createModelCounterStatIfAbsent(modelId, ActionName.DEPLOY, ML_ACTION_REQUEST_COUNT).increment();
List<String> workerNodes = mlTask.getWorkerNodes();
if (modelCacheHelper.isModelDeployed(modelId)) {
if (!autoDeployModel && workerNodes != null && workerNodes.size() > 0) {
log.info("Set new target node ids {} for model {}", Arrays.toString(workerNodes.toArray(new String[0])), modelId);
modelCacheHelper.setDeployToAllNodes(modelId, deployToAllNodes);
modelCacheHelper.setTargetWorkerNodes(modelId, workerNodes);
modelCacheHelper.refreshLastAccessTime(modelId);
}
listener.onResponse("successful");
return;
}
if (modelCacheHelper.getLocalDeployedModels().length >= maxModelPerNode) {
listener.onFailure(new IllegalArgumentException("Exceed max local model per node limit"));
return;
}
int eligibleNodeCount = workerNodes.size();
if (!autoDeployModel) {
modelCacheHelper.initModelState(modelId, MLModelState.DEPLOYING, functionName, workerNodes, deployToAllNodes);
} else {
modelCacheHelper.initModelStateAutoDeploy(modelId, MLModelState.DEPLOYING, functionName, workerNodes);
}
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
ActionListener<String> wrappedListener = ActionListener.runBefore(listener, () -> {
context.restore();
modelCacheHelper.removeAutoDeployModel(modelId);
modelCacheHelper.setIsAutoDeploying(modelId, false);