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[ML] Update the number of allocations per nlp process (#86277)
Adds a method to DeploymentManager to update the number of allocations per process as implemented in elastic/ml-cpp#2258.
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111 changes: 111 additions & 0 deletions
111
.../src/main/java/org/elasticsearch/xpack/ml/inference/deployment/AbstractPyTorchAction.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,111 @@ | ||
| /* | ||
| * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one | ||
| * or more contributor license agreements. Licensed under the Elastic License | ||
| * 2.0; you may not use this file except in compliance with the Elastic License | ||
| * 2.0. | ||
| */ | ||
|
|
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| package org.elasticsearch.xpack.ml.inference.deployment; | ||
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| import org.apache.logging.log4j.Logger; | ||
| import org.apache.logging.log4j.message.ParameterizedMessage; | ||
| import org.elasticsearch.ElasticsearchStatusException; | ||
| import org.elasticsearch.action.ActionListener; | ||
| import org.elasticsearch.common.util.concurrent.AbstractRunnable; | ||
| import org.elasticsearch.core.TimeValue; | ||
| import org.elasticsearch.rest.RestStatus; | ||
| import org.elasticsearch.threadpool.Scheduler; | ||
| import org.elasticsearch.threadpool.ThreadPool; | ||
| import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper; | ||
| import org.elasticsearch.xpack.ml.MachineLearning; | ||
|
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| import java.util.concurrent.atomic.AtomicBoolean; | ||
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| abstract class AbstractPyTorchAction<T> extends AbstractRunnable { | ||
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| private final String modelId; | ||
| private final long requestId; | ||
| private final TimeValue timeout; | ||
| private final Scheduler.Cancellable timeoutHandler; | ||
| private final DeploymentManager.ProcessContext processContext; | ||
| private final AtomicBoolean notified = new AtomicBoolean(); | ||
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| private final ActionListener<T> listener; | ||
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| protected AbstractPyTorchAction( | ||
| String modelId, | ||
| long requestId, | ||
| TimeValue timeout, | ||
| DeploymentManager.ProcessContext processContext, | ||
| ThreadPool threadPool, | ||
| ActionListener<T> listener | ||
| ) { | ||
| this.modelId = modelId; | ||
| this.requestId = requestId; | ||
| this.timeout = timeout; | ||
| this.timeoutHandler = threadPool.schedule( | ||
| this::onTimeout, | ||
| ExceptionsHelper.requireNonNull(timeout, "timeout"), | ||
| MachineLearning.UTILITY_THREAD_POOL_NAME | ||
| ); | ||
| this.processContext = processContext; | ||
| this.listener = listener; | ||
| } | ||
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| void onTimeout() { | ||
| if (notified.compareAndSet(false, true)) { | ||
| processContext.getTimeoutCount().incrementAndGet(); | ||
| processContext.getResultProcessor().ignoreResponseWithoutNotifying(String.valueOf(requestId)); | ||
| listener.onFailure( | ||
| new ElasticsearchStatusException("timeout [{}] waiting for inference result", RestStatus.REQUEST_TIMEOUT, timeout) | ||
| ); | ||
| return; | ||
| } | ||
| getLogger().debug("[{}] request [{}] received timeout after [{}] but listener already alerted", modelId, requestId, timeout); | ||
| } | ||
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| void onSuccess(T result) { | ||
| timeoutHandler.cancel(); | ||
| if (notified.compareAndSet(false, true)) { | ||
| listener.onResponse(result); | ||
| return; | ||
| } | ||
| getLogger().debug("[{}] request [{}] received inference response but listener already notified", modelId, requestId); | ||
| } | ||
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| @Override | ||
| public void onFailure(Exception e) { | ||
| timeoutHandler.cancel(); | ||
| if (notified.compareAndSet(false, true)) { | ||
| processContext.getResultProcessor().ignoreResponseWithoutNotifying(String.valueOf(requestId)); | ||
| listener.onFailure(e); | ||
| return; | ||
| } | ||
| getLogger().debug( | ||
| () -> new ParameterizedMessage("[{}] request [{}] received failure but listener already notified", modelId, requestId), | ||
| e | ||
| ); | ||
| } | ||
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| protected void onFailure(String errorMessage) { | ||
| onFailure(new ElasticsearchStatusException("Error in inference process: [" + errorMessage + "]", RestStatus.INTERNAL_SERVER_ERROR)); | ||
| } | ||
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| boolean isNotified() { | ||
| return notified.get(); | ||
| } | ||
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| long getRequestId() { | ||
| return requestId; | ||
| } | ||
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| String getModelId() { | ||
| return modelId; | ||
| } | ||
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| DeploymentManager.ProcessContext getProcessContext() { | ||
| return processContext; | ||
| } | ||
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| protected abstract Logger getLogger(); | ||
| } |
103 changes: 103 additions & 0 deletions
103
...ain/java/org/elasticsearch/xpack/ml/inference/deployment/ControlMessagePyTorchAction.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| /* | ||
| * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one | ||
| * or more contributor license agreements. Licensed under the Elastic License | ||
| * 2.0; you may not use this file except in compliance with the Elastic License | ||
| * 2.0. | ||
| */ | ||
|
|
||
| package org.elasticsearch.xpack.ml.inference.deployment; | ||
|
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| import org.apache.logging.log4j.LogManager; | ||
| import org.apache.logging.log4j.Logger; | ||
| import org.apache.logging.log4j.message.ParameterizedMessage; | ||
| import org.elasticsearch.action.ActionListener; | ||
| import org.elasticsearch.common.bytes.BytesReference; | ||
| import org.elasticsearch.core.TimeValue; | ||
| import org.elasticsearch.threadpool.ThreadPool; | ||
| import org.elasticsearch.xcontent.XContentBuilder; | ||
| import org.elasticsearch.xcontent.XContentFactory; | ||
| import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper; | ||
| import org.elasticsearch.xpack.ml.inference.pytorch.results.PyTorchResult; | ||
| import org.elasticsearch.xpack.ml.inference.pytorch.results.ThreadSettings; | ||
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| import java.io.IOException; | ||
|
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| class ControlMessagePyTorchAction extends AbstractPyTorchAction<ThreadSettings> { | ||
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| private static final Logger logger = LogManager.getLogger(InferencePyTorchAction.class); | ||
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| private final int numAllocationThreads; | ||
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| private enum ControlMessageTypes { | ||
| AllocationThreads | ||
| }; | ||
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| ControlMessagePyTorchAction( | ||
| String modelId, | ||
| long requestId, | ||
| int numAllocationThreads, | ||
| TimeValue timeout, | ||
| DeploymentManager.ProcessContext processContext, | ||
| ThreadPool threadPool, | ||
| ActionListener<ThreadSettings> listener | ||
| ) { | ||
| super(modelId, requestId, timeout, processContext, threadPool, listener); | ||
| this.numAllocationThreads = numAllocationThreads; | ||
| } | ||
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| @Override | ||
| protected void doRun() throws Exception { | ||
| if (isNotified()) { | ||
| // Should not execute request as it has already timed out while waiting in the queue | ||
| logger.debug( | ||
| () -> new ParameterizedMessage( | ||
| "[{}] skipping control message on request [{}] as it has timed out", | ||
| getModelId(), | ||
| getRequestId() | ||
| ) | ||
| ); | ||
| return; | ||
| } | ||
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| final String requestIdStr = String.valueOf(getRequestId()); | ||
| try { | ||
| var message = buildControlMessage(requestIdStr, numAllocationThreads); | ||
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| getProcessContext().getResultProcessor() | ||
| .registerRequest(requestIdStr, ActionListener.wrap(this::processResponse, this::onFailure)); | ||
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| getProcessContext().getProcess().get().writeInferenceRequest(message); | ||
| } catch (IOException e) { | ||
| logger.error(new ParameterizedMessage("[{}] error writing control message to the inference process", getModelId()), e); | ||
| onFailure(ExceptionsHelper.serverError("Error writing control message to the inference process", e)); | ||
| } catch (Exception e) { | ||
| onFailure(e); | ||
| } | ||
| } | ||
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| public static BytesReference buildControlMessage(String requestId, int numAllocationThreads) throws IOException { | ||
| XContentBuilder builder = XContentFactory.jsonBuilder(); | ||
| builder.startObject(); | ||
| builder.field("request_id", requestId); | ||
| builder.field("control", ControlMessageTypes.AllocationThreads.ordinal()); | ||
| builder.field("num_allocations", numAllocationThreads); | ||
| builder.endObject(); | ||
|
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| // BytesReference.bytes closes the builder | ||
| return BytesReference.bytes(builder); | ||
| } | ||
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| public void processResponse(PyTorchResult result) { | ||
| if (result.isError()) { | ||
| onFailure(result.errorResult().error()); | ||
| return; | ||
| } | ||
| onSuccess(result.threadSettings()); | ||
| } | ||
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| @Override | ||
| protected Logger getLogger() { | ||
| return logger; | ||
| } | ||
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| } |
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