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Fix user defined preprocess function missing prediction issue #2418

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merged 5 commits into from
May 9, 2024
Merged

Fix user defined preprocess function missing prediction issue #2418

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b6fa75f
Fix user defined preprocess function missing prediction issue
zane-neo May 8, 2024
284bb98
Add validation to predictAction in connector
zane-neo May 8, 2024
410f42b
Add check to multi-modal non image case
zane-neo May 8, 2024
3ec9934
add UTs
zane-neo May 8, 2024
ae74167
format code
zane-neo May 8, 2024
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12 changes: 12 additions & 0 deletions ...hms/src/main/java/org/opensearch/ml/engine/algorithms/remote/RemoteConnectorExecutor.java
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Optional;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.atomic.AtomicReference;
Expand All @@ -27,6 +28,8 @@
import org.opensearch.core.xcontent.NamedXContentRegistry;
import org.opensearch.ml.common.FunctionName;
import org.opensearch.ml.common.connector.Connector;
import org.opensearch.ml.common.connector.ConnectorAction;
import org.opensearch.ml.common.connector.MLPreProcessFunction;
import org.opensearch.ml.common.dataset.MLInputDataset;
import org.opensearch.ml.common.dataset.TextDocsInputDataSet;
import org.opensearch.ml.common.dataset.remote.RemoteInferenceInputDataSet;
Expand Down Expand Up @@ -99,6 +102,15 @@ private Tuple<Integer, Integer> calculateChunkSize(TextDocsInputDataSet textDocs
return Tuple.tuple(textDocsLength / stepSize + 1, stepSize);
}
} else {
Optional<ConnectorAction> predictAction = getConnector().findPredictAction();
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Can we have unit test for this section?

if (predictAction.isEmpty()) {
throw new IllegalArgumentException("no predict action found");
}
String preProcessFunction = predictAction.get().getPreProcessFunction();
if (preProcessFunction != null && !MLPreProcessFunction.contains(preProcessFunction)) {
// user defined preprocess script, this case, the chunk size is always equals to text docs length.
return Tuple.tuple(textDocsLength, 1);
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Is this understanding correct ?

  1. textDocsLength means how many chunks
  2. 1 means step or chunk size ?

If correct, why hard code the chunk size as 1 ? This issue #2417 is an example, it's not the only case. For example some user may process two documents in one chunk.

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https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/bedrock_connector_titan_multimodal_embedding_blueprint.md

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textDocsLength is chunks, 1 means step. When user process two documents in one chunk, user has to specify the input_docs_processed_step_size, in this case, this won't enter this branch. Multi-modal is a case that user needs to handle two documents in one chunk, I've tested this case and it works well.

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Thanks for testing multi-modal case.
It's possible that step size > 1 and no input_docs_processed_step_size. For example, before async http client, user can confiture pre_process function to read three documents and construct "inputs": [doc1, doc2, doc3], when response comes back, will move to next 3 docs

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This is a rare case, and we have workaround which is to add input_docs_processed_step_size configuration.

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Thanks, based on our discussion, this sounds good

}
// consider as batch.
return Tuple.tuple(1, textDocsLength);
}
Expand Down
84 changes: 84 additions & 0 deletions ...ms/src/test/java/org/opensearch/ml/engine/algorithms/remote/AwsConnectorExecutorTest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
package org.opensearch.ml.engine.algorithms.remote;

import static org.junit.Assert.assertEquals;
import static org.mockito.ArgumentMatchers.any;
import static org.mockito.Mockito.spy;
import static org.mockito.Mockito.times;
import static org.mockito.Mockito.when;
Expand All @@ -30,6 +31,7 @@
import org.opensearch.common.settings.Settings;
import org.opensearch.common.util.concurrent.ThreadContext;
import org.opensearch.core.action.ActionListener;
import org.opensearch.ingest.TestTemplateService;
import org.opensearch.ml.common.FunctionName;
import org.opensearch.ml.common.connector.AwsConnector;
import org.opensearch.ml.common.connector.Connector;
Expand All @@ -42,6 +44,7 @@
import org.opensearch.ml.common.transport.MLTaskResponse;
import org.opensearch.ml.engine.encryptor.Encryptor;
import org.opensearch.ml.engine.encryptor.EncryptorImpl;
import org.opensearch.script.ScriptService;
import org.opensearch.threadpool.ThreadPool;

import com.google.common.collect.ImmutableList;
Expand All @@ -67,10 +70,15 @@ public class AwsConnectorExecutorTest {

Encryptor encryptor;

@Mock
private ScriptService scriptService;

@Before
public void setUp() {
MockitoAnnotations.openMocks(this);
encryptor = new EncryptorImpl("m+dWmfmnNRiNlOdej/QelEkvMTyH//frS2TBeS2BP4w=");
when(scriptService.compile(any(), any()))
.then(invocation -> new TestTemplateService.MockTemplateScript.Factory("{\"result\": \"hello world\"}"));
}

@Test
Expand Down Expand Up @@ -282,4 +290,80 @@ public void executePredict_RemoteInferenceInput_negativeStepSize_throwIllegalArg
Mockito.verify(actionListener, times(1)).onFailure(exceptionCaptor.capture());
assert exceptionCaptor.getValue() instanceof IllegalArgumentException;
}

@Test
public void executePredict_TextDocsInferenceInput_withoutStepSize_emptyPredictionAction() {
ConnectorAction predictAction = ConnectorAction
.builder()
.actionType(ConnectorAction.ActionType.PREDICT)
.method("POST")
.url("http://openai.com/mock")
.requestBody("{\"input\": ${parameters.input}}")
.preProcessFunction(MLPreProcessFunction.TEXT_DOCS_TO_OPENAI_EMBEDDING_INPUT)
.build();
Map<String, String> credential = ImmutableMap
.of(ACCESS_KEY_FIELD, encryptor.encrypt("test_key"), SECRET_KEY_FIELD, encryptor.encrypt("test_secret_key"));
Map<String, String> parameters = ImmutableMap.of(REGION_FIELD, "us-west-2", SERVICE_NAME_FIELD, "sagemaker");
Connector connector = AwsConnector
.awsConnectorBuilder()
.name("test connector")
.version("1")
.protocol("http")
.parameters(parameters)
.credential(credential)
.build();
connector.decrypt((c) -> encryptor.decrypt(c));
AwsConnectorExecutor executor = spy(new AwsConnectorExecutor(connector));
Settings settings = Settings.builder().build();
threadContext = new ThreadContext(settings);
when(executor.getClient()).thenReturn(client);
when(client.threadPool()).thenReturn(threadPool);
when(threadPool.getThreadContext()).thenReturn(threadContext);

MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(ImmutableList.of("input1", "input2", "input3")).build();
executor
.executePredict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build(), actionListener);
ArgumentCaptor<Exception> exceptionArgumentCaptor = ArgumentCaptor.forClass(Exception.class);
Mockito.verify(actionListener, times(1)).onFailure(exceptionArgumentCaptor.capture());
assert exceptionArgumentCaptor.getValue() instanceof IllegalArgumentException;
assert "no predict action found".equals(exceptionArgumentCaptor.getValue().getMessage());
}

@Test
public void executePredict_TextDocsInferenceInput_withoutStepSize_userDefinedPreProcessFunction() {
ConnectorAction predictAction = ConnectorAction
.builder()
.actionType(ConnectorAction.ActionType.PREDICT)
.method("POST")
.url("http://openai.com/mock")
.requestBody("{\"input\": ${parameters.input}}")
.preProcessFunction(
"\n StringBuilder builder = new StringBuilder();\n builder.append(\"\\\"\");\n String first = params.text_docs[0];\n builder.append(first);\n builder.append(\"\\\"\");\n def parameters = \"{\" +\"\\\"text_inputs\\\":\" + builder + \"}\";\n return \"{\" +\"\\\"parameters\\\":\" + parameters + \"}\";"
)
.build();
Map<String, String> credential = ImmutableMap
.of(ACCESS_KEY_FIELD, encryptor.encrypt("test_key"), SECRET_KEY_FIELD, encryptor.encrypt("test_secret_key"));
Map<String, String> parameters = ImmutableMap.of(REGION_FIELD, "us-west-2", SERVICE_NAME_FIELD, "sagemaker");
Connector connector = AwsConnector
.awsConnectorBuilder()
.name("test connector")
.version("1")
.protocol("http")
.parameters(parameters)
.credential(credential)
.actions(Arrays.asList(predictAction))
.build();
connector.decrypt((c) -> encryptor.decrypt(c));
AwsConnectorExecutor executor = spy(new AwsConnectorExecutor(connector));
Settings settings = Settings.builder().build();
threadContext = new ThreadContext(settings);
when(executor.getClient()).thenReturn(client);
when(client.threadPool()).thenReturn(threadPool);
when(threadPool.getThreadContext()).thenReturn(threadContext);
when(executor.getScriptService()).thenReturn(scriptService);

MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(ImmutableList.of("input1", "input2", "input3")).build();
executor
.executePredict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build(), actionListener);
}
}
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