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Backport from 2.11 to 2.x #1728

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Dec 1, 2023
Merged

Backport from 2.11 to 2.x #1728

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d07ec5f
support step size for embedding model which outputs less embeddings (…
ylwu-amzn Nov 3, 2023
4e00865
validate step size (#1587)
ylwu-amzn Nov 3, 2023
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16 changes: 15 additions & 1 deletion ...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 @@ -48,7 +48,21 @@ default ModelTensorOutput executePredict(MLInput mlInput) {
if (tempTensorOutputs.size() > 0 && tempTensorOutputs.get(0).getMlModelTensors() != null) {
tensorCount = tempTensorOutputs.get(0).getMlModelTensors().size();
}
processedDocs += Math.max(tensorCount, 1);
// This is to support some model which takes N text docs and embedding size is less than N.
// We need to tell executor what's the step size for each model run.
Map<String, String> parameters = getConnector().getParameters();
if (parameters != null && parameters.containsKey("input_docs_processed_step_size")) {
int stepSize = Integer.parseInt(parameters.get("input_docs_processed_step_size"));
// We need to check the parameter on runtime as parameter can be passed into predict request
if (stepSize <= 0) {
throw new IllegalArgumentException(
"Invalid parameter: input_docs_processed_step_size. It must be positive integer."
);
}
processedDocs += stepSize;
} else {
processedDocs += Math.max(tensorCount, 1);
}
tensorOutputs.addAll(tempTensorOutputs);
}
} else {
Expand Down
133 changes: 132 additions & 1 deletion ...c/test/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutorTest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@

import java.io.IOException;
import java.util.Arrays;
import java.util.Map;

import org.apache.http.HttpEntity;
import org.apache.http.ProtocolVersion;
Expand Down Expand Up @@ -201,7 +202,7 @@ public void executePredict_TextDocsInput() throws IOException {
.postProcessFunction(MLPostProcessFunction.OPENAI_EMBEDDING)
.requestBody("{\"input\": ${parameters.input}}")
.build();
Connector connector = HttpConnector
HttpConnector connector = HttpConnector
.builder()
.name("test connector")
.version("1")
Expand Down Expand Up @@ -244,6 +245,7 @@ public void executePredict_TextDocsInput() throws IOException {
HttpEntity entity = new StringEntity(modelResponse);
when(response.getEntity()).thenReturn(entity);
when(executor.getHttpClient()).thenReturn(httpClient);
when(executor.getConnector()).thenReturn(connector);
MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("test doc1", "test doc2")).build();
ModelTensorOutput modelTensorOutput = executor
.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
Expand All @@ -261,4 +263,133 @@ public void executePredict_TextDocsInput() throws IOException {
modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(1).getData()
);
}

@Test
public void executePredict_TextDocsInput_LessEmbeddingThanInputDocs() throws IOException {
String preprocessResult1 = "{\"parameters\": { \"input\": \"test doc1\" } }";
String preprocessResult2 = "{\"parameters\": { \"input\": \"test doc2\" } }";
when(scriptService.compile(any(), any()))
.then(invocation -> new TestTemplateService.MockTemplateScript.Factory(preprocessResult1))
.then(invocation -> new TestTemplateService.MockTemplateScript.Factory(preprocessResult2));

ConnectorAction predictAction = ConnectorAction
.builder()
.actionType(ConnectorAction.ActionType.PREDICT)
.method("POST")
.url("http://test.com/mock")
.preProcessFunction(MLPreProcessFunction.TEXT_DOCS_TO_OPENAI_EMBEDDING_INPUT)
.postProcessFunction(MLPostProcessFunction.OPENAI_EMBEDDING)
.requestBody("{\"input\": ${parameters.input}}")
.build();
Map<String, String> parameters = ImmutableMap.of("input_docs_processed_step_size", "2");
HttpConnector connector = HttpConnector
.builder()
.name("test connector")
.version("1")
.protocol("http")
.parameters(parameters)
.actions(Arrays.asList(predictAction))
.build();
HttpJsonConnectorExecutor executor = spy(new HttpJsonConnectorExecutor(connector));
executor.setScriptService(scriptService);
when(httpClient.execute(any())).thenReturn(response);
// model takes 2 input docs, but only output 1 embedding
String modelResponse = "{\n"
+ " \"object\": \"list\",\n"
+ " \"data\": [\n"
+ " {\n"
+ " \"object\": \"embedding\",\n"
+ " \"index\": 0,\n"
+ " \"embedding\": [\n"
+ " -0.014555434,\n"
+ " -0.002135904,\n"
+ " 0.0035105038\n"
+ " ]\n"
+ " } ],\n"
+ " \"model\": \"text-embedding-ada-002-v2\",\n"
+ " \"usage\": {\n"
+ " \"prompt_tokens\": 5,\n"
+ " \"total_tokens\": 5\n"
+ " }\n"
+ "}";
StatusLine statusLine = new BasicStatusLine(new ProtocolVersion("HTTP", 1, 1), 200, "OK");
when(response.getStatusLine()).thenReturn(statusLine);
HttpEntity entity = new StringEntity(modelResponse);
when(response.getEntity()).thenReturn(entity);
when(executor.getHttpClient()).thenReturn(httpClient);
when(executor.getConnector()).thenReturn(connector);
MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("test doc1", "test doc2")).build();
ModelTensorOutput modelTensorOutput = executor
.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().size());
Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().size());
Assert.assertEquals("sentence_embedding", modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getName());
Assert
.assertArrayEquals(
new Number[] { -0.014555434, -0.002135904, 0.0035105038 },
modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getData()
);
}

@Test
public void executePredict_TextDocsInput_LessEmbeddingThanInputDocs_InvalidStepSize() throws IOException {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("Invalid parameter: input_docs_processed_step_size. It must be positive integer.");
String preprocessResult1 = "{\"parameters\": { \"input\": \"test doc1\" } }";
String preprocessResult2 = "{\"parameters\": { \"input\": \"test doc2\" } }";
when(scriptService.compile(any(), any()))
.then(invocation -> new TestTemplateService.MockTemplateScript.Factory(preprocessResult1))
.then(invocation -> new TestTemplateService.MockTemplateScript.Factory(preprocessResult2));

ConnectorAction predictAction = ConnectorAction
.builder()
.actionType(ConnectorAction.ActionType.PREDICT)
.method("POST")
.url("http://test.com/mock")
.preProcessFunction(MLPreProcessFunction.TEXT_DOCS_TO_OPENAI_EMBEDDING_INPUT)
.postProcessFunction(MLPostProcessFunction.OPENAI_EMBEDDING)
.requestBody("{\"input\": ${parameters.input}}")
.build();
// step size must be positive integer, here we set it as -1, should trigger IllegalArgumentException
Map<String, String> parameters = ImmutableMap.of("input_docs_processed_step_size", "-1");
HttpConnector connector = HttpConnector
.builder()
.name("test connector")
.version("1")
.protocol("http")
.parameters(parameters)
.actions(Arrays.asList(predictAction))
.build();
HttpJsonConnectorExecutor executor = spy(new HttpJsonConnectorExecutor(connector));
executor.setScriptService(scriptService);
when(httpClient.execute(any())).thenReturn(response);
// model takes 2 input docs, but only output 1 embedding
String modelResponse = "{\n"
+ " \"object\": \"list\",\n"
+ " \"data\": [\n"
+ " {\n"
+ " \"object\": \"embedding\",\n"
+ " \"index\": 0,\n"
+ " \"embedding\": [\n"
+ " -0.014555434,\n"
+ " -0.002135904,\n"
+ " 0.0035105038\n"
+ " ]\n"
+ " } ],\n"
+ " \"model\": \"text-embedding-ada-002-v2\",\n"
+ " \"usage\": {\n"
+ " \"prompt_tokens\": 5,\n"
+ " \"total_tokens\": 5\n"
+ " }\n"
+ "}";
StatusLine statusLine = new BasicStatusLine(new ProtocolVersion("HTTP", 1, 1), 200, "OK");
when(response.getStatusLine()).thenReturn(statusLine);
HttpEntity entity = new StringEntity(modelResponse);
when(response.getEntity()).thenReturn(entity);
when(executor.getHttpClient()).thenReturn(httpClient);
when(executor.getConnector()).thenReturn(connector);
MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("test doc1", "test doc2")).build();
ModelTensorOutput modelTensorOutput = executor
.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
}
}
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