Added more detailed error messages for KNN model training

CHANGELOG.md

@@ -23,6 +23,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 - Introduced a writing layer in native engines where relies on the writing interface to process IO. (#2241)[https://github.com/opensearch-project/k-NN/pull/2241]
 - Allow method parameter override for training based indices (#2290) https://github.com/opensearch-project/k-NN/pull/2290]
 - Optimizes lucene query execution to prevent unnecessary rewrites (#2305)[https://github.com/opensearch-project/k-NN/pull/2305]
+- Added more detailed error messages for KNN model training (#2378)[https://github.com/opensearch-project/k-NN/pull/2378]
 - Add check to directly use ANN Search when filters match all docs. (#2320)[https://github.com/opensearch-project/k-NN/pull/2320]
 - Use one formula to calculate cosine similarity (#2357)[https://github.com/opensearch-project/k-NN/pull/2357]
 - Remove DocsWithFieldSet reference from NativeEngineFieldVectorsWriter (#2408)[https://github.com/opensearch-project/k-NN/pull/2408]

qa/restart-upgrade/src/test/java/org/opensearch/knn/bwc/FaissSQIT.java

@@ -225,7 +225,7 @@ public void testIVFSQFP16_onUpgradeWhenIndexedAndQueried_thenSucceed() throws Ex
 
             // Add training data
             createBasicKnnIndex(TRAIN_INDEX, TRAIN_TEST_FIELD, DIMENSION);
-            int trainingDataCount = 200;
+            int trainingDataCount = 1100;
             bulkIngestRandomVectors(TRAIN_INDEX, TRAIN_TEST_FIELD, trainingDataCount, DIMENSION);
 
             XContentBuilder builder = XContentFactory.jsonBuilder()
@@ -278,7 +278,7 @@ public void testIVFSQFP16_onUpgradeWhenClipToFp16isTrueAndIndexedWithOutOfFP16Ra
 
             // Add training data
             createBasicKnnIndex(TRAIN_INDEX, TRAIN_TEST_FIELD, dimension);
-            int trainingDataCount = 200;
+            int trainingDataCount = 1100;
             bulkIngestRandomVectors(TRAIN_INDEX, TRAIN_TEST_FIELD, trainingDataCount, dimension);
 
             XContentBuilder builder = XContentFactory.jsonBuilder()

src/main/java/org/opensearch/knn/index/engine/AbstractKNNMethod.java

@@ -22,6 +22,12 @@
 import java.util.Locale;
 import java.util.Map;
 import java.util.Set;
+import java.util.function.Function;
+
+import static org.opensearch.knn.common.KNNConstants.ENCODER_PARAMETER_PQ_M;
+import static org.opensearch.knn.common.KNNConstants.METHOD_ENCODER_PARAMETER;
+import static org.opensearch.knn.common.KNNConstants.METHOD_PARAMETER_NLIST;
+import static org.opensearch.knn.common.KNNConstants.ENCODER_PARAMETER_PQ_CODE_SIZE;
 
 /**
  * Abstract class for KNN methods. This class provides the common functionality for all KNN methods.
@@ -108,6 +114,55 @@ protected PerDimensionProcessor doGetPerDimensionProcessor(
         return PerDimensionProcessor.NOOP_PROCESSOR;
     }
 
+    protected Function<TrainingConfigValidationInput, TrainingConfigValidationOutput> doGetTrainingConfigValidationSetup() {
+        return (trainingConfigValidationInput) -> {
+
+            KNNMethodContext knnMethodContext = trainingConfigValidationInput.getKnnMethodContext();
+            KNNMethodConfigContext knnMethodConfigContext = trainingConfigValidationInput.getKnnMethodConfigContext();
+            Long trainingVectors = trainingConfigValidationInput.getTrainingVectorsCount();
+
+            TrainingConfigValidationOutput.TrainingConfigValidationOutputBuilder builder = TrainingConfigValidationOutput.builder();
+
+            // validate ENCODER_PARAMETER_PQ_M is divisible by vector dimension
+            if (knnMethodContext != null && knnMethodConfigContext != null) {
+                if (knnMethodContext.getMethodComponentContext().getParameters().containsKey(ENCODER_PARAMETER_PQ_M)
+                    && knnMethodConfigContext.getDimension() % (Integer) knnMethodContext.getMethodComponentContext()
+                        .getParameters()
+                        .get(ENCODER_PARAMETER_PQ_M) != 0) {
+                    builder.valid(false);
+                    return builder.build();
+                } else {
+                    builder.valid(true);
+                }
+            }
+
+            // validate number of training points should be greater than minimum clustering criteria defined in faiss
+            if (knnMethodContext != null && trainingVectors != null) {
+                long minTrainingVectorCount = 1000;
+
+                MethodComponentContext encoderContext = (MethodComponentContext) knnMethodContext.getMethodComponentContext()
+                    .getParameters()
+                    .get(METHOD_ENCODER_PARAMETER);
+
+                if (knnMethodContext.getMethodComponentContext().getParameters().containsKey(METHOD_PARAMETER_NLIST)
+                    && encoderContext.getParameters().containsKey(ENCODER_PARAMETER_PQ_CODE_SIZE)) {
+
+                    int nlist = ((Integer) knnMethodContext.getMethodComponentContext().getParameters().get(METHOD_PARAMETER_NLIST));
+                    int code_size = ((Integer) encoderContext.getParameters().get(ENCODER_PARAMETER_PQ_CODE_SIZE));
+                    minTrainingVectorCount = (long) Math.max(nlist, Math.pow(2, code_size));
+                }
+
+                if (trainingVectors < minTrainingVectorCount) {
+                    builder.valid(false).minTrainingVectorCount(minTrainingVectorCount);
+                    return builder.build();
+                } else {
+                    builder.valid(true);
+                }
+            }
+            return builder.build();
+        };
+    }
+
     protected VectorTransformer getVectorTransformer(SpaceType spaceType) {
         return VectorTransformerFactory.NOOP_VECTOR_TRANSFORMER;
     }
@@ -131,6 +186,7 @@ public KNNLibraryIndexingContext getKNNLibraryIndexingContext(
             .perDimensionValidator(doGetPerDimensionValidator(knnMethodContext, knnMethodConfigContext))
             .perDimensionProcessor(doGetPerDimensionProcessor(knnMethodContext, knnMethodConfigContext))
             .vectorTransformer(getVectorTransformer(knnMethodContext.getSpaceType()))
+            .trainingConfigValidationSetup(doGetTrainingConfigValidationSetup())
             .build();
     }
 

src/main/java/org/opensearch/knn/index/engine/KNNLibraryIndexingContext.java

@@ -12,6 +12,7 @@
 import org.opensearch.knn.index.mapper.VectorValidator;
 
 import java.util.Map;
+import java.util.function.Function;
 
 /**
  * Context a library gives to build one of its indices
@@ -49,6 +50,12 @@ public interface KNNLibraryIndexingContext {
      */
     PerDimensionProcessor getPerDimensionProcessor();
 
+    /**
+     *
+     * @return Get function that validates training model parameters
+     */
+    Function<TrainingConfigValidationInput, TrainingConfigValidationOutput> getTrainingConfigValidationSetup();
+
     /**
      * Get the vector transformer that will be used to transform the vector before indexing.
      * This will be applied at vector level once entire vector is parsed and validated.

src/main/java/org/opensearch/knn/index/engine/KNNLibraryIndexingContextImpl.java

@@ -14,6 +14,7 @@
 
 import java.util.Collections;
 import java.util.Map;
+import java.util.function.Function;
 
 /**
  * Simple implementation of {@link KNNLibraryIndexingContext}
@@ -29,6 +30,7 @@ public class KNNLibraryIndexingContextImpl implements KNNLibraryIndexingContext
     private Map<String, Object> parameters = Collections.emptyMap();
     @Builder.Default
     private QuantizationConfig quantizationConfig = QuantizationConfig.EMPTY;
+    private Function<TrainingConfigValidationInput, TrainingConfigValidationOutput> trainingConfigValidationSetup;
 
     @Override
     public Map<String, Object> getLibraryParameters() {
@@ -59,4 +61,9 @@ public PerDimensionValidator getPerDimensionValidator() {
     public PerDimensionProcessor getPerDimensionProcessor() {
         return perDimensionProcessor;
     }
+
+    @Override
+    public Function<TrainingConfigValidationInput, TrainingConfigValidationOutput> getTrainingConfigValidationSetup() {
+        return trainingConfigValidationSetup;
+    }
 }

src/main/java/org/opensearch/knn/index/engine/TrainingConfigValidationInput.java

@@ -0,0 +1,26 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.knn.index.engine;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Getter;
+import lombok.Setter;
+
+/**
+ * This object provides the input of the validation checks for training model inputs.
+ * The values in this object need to be dynamically set and calling code needs to handle
+ * the possibility that the values have not been set.
+ */
+@Setter
+@Getter
+@Builder
+@AllArgsConstructor
+public class TrainingConfigValidationInput {
+    private Long trainingVectorsCount;
+    private KNNMethodContext knnMethodContext;
+    private KNNMethodConfigContext knnMethodConfigContext;
+}

src/main/java/org/opensearch/knn/index/engine/TrainingConfigValidationOutput.java

@@ -0,0 +1,25 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.knn.index.engine;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Getter;
+import lombok.Setter;
+
+/**
+ * This object provides the output of the validation checks for training model inputs.
+ * The values in this object need to be dynamically set and calling code needs to handle
+ * the possibility that the values have not been set.
+ */
+@Setter
+@Getter
+@Builder
+@AllArgsConstructor
+public class TrainingConfigValidationOutput {
+    private boolean valid;
+    private long minTrainingVectorCount;
+}

src/main/java/org/opensearch/knn/plugin/transport/TrainingJobRouterTransportAction.java

@@ -23,12 +23,18 @@
 import org.opensearch.common.ValidationException;
 import org.opensearch.common.inject.Inject;
 import org.opensearch.knn.index.VectorDataType;
+import org.opensearch.knn.index.engine.KNNLibraryIndexingContext;
+import org.opensearch.knn.index.engine.KNNMethodConfigContext;
+import org.opensearch.knn.index.engine.KNNMethodContext;
+import org.opensearch.knn.index.engine.TrainingConfigValidationOutput;
+import org.opensearch.knn.index.engine.TrainingConfigValidationInput;
 import org.opensearch.search.builder.SearchSourceBuilder;
 import org.opensearch.tasks.Task;
 import org.opensearch.transport.TransportRequestOptions;
 import org.opensearch.transport.TransportService;
 
 import java.util.Map;
+import java.util.function.Function;
 
 import static org.opensearch.knn.common.KNNConstants.BYTES_PER_KILOBYTES;
 import static org.opensearch.search.internal.SearchContext.DEFAULT_TERMINATE_AFTER;
@@ -134,6 +140,29 @@ protected void getTrainingIndexSizeInKB(TrainingModelRequest trainingModelReques
                 trainingVectors = trainingModelRequest.getMaximumVectorCount();
             }
 
+            KNNMethodContext knnMethodContext = trainingModelRequest.getKnnMethodContext();
+            KNNMethodConfigContext knnMethodConfigContext = trainingModelRequest.getKnnMethodConfigContext();
+
+            KNNLibraryIndexingContext knnLibraryIndexingContext = knnMethodContext.getKnnEngine()
+                .getKNNLibraryIndexingContext(knnMethodContext, knnMethodConfigContext);
+
+            Function<TrainingConfigValidationInput, TrainingConfigValidationOutput> validateTrainingConfig = knnLibraryIndexingContext
+                .getTrainingConfigValidationSetup();
+
+            TrainingConfigValidationInput.TrainingConfigValidationInputBuilder inputBuilder = TrainingConfigValidationInput.builder();
+
+            TrainingConfigValidationOutput validation = validateTrainingConfig.apply(
+                inputBuilder.trainingVectorsCount(trainingVectors).knnMethodContext(knnMethodContext).build()
+            );
+            if (!validation.isValid()) {
+                ValidationException exception = new ValidationException();
+                exception.addValidationError(
+                    String.format("Number of training points should be greater than %d", validation.getMinTrainingVectorCount())
+                );
+                listener.onFailure(exception);
+                return;
+            }
+
             listener.onResponse(
                 estimateVectorSetSizeInKB(trainingVectors, trainingModelRequest.getDimension(), trainingModelRequest.getVectorDataType())
             );

src/main/java/org/opensearch/knn/plugin/transport/TrainingModelRequest.java

@@ -30,10 +30,14 @@
 import org.opensearch.knn.index.engine.EngineResolver;
 import org.opensearch.knn.index.util.IndexUtil;
 import org.opensearch.knn.index.engine.KNNMethodContext;
+import org.opensearch.knn.index.engine.KNNLibraryIndexingContext;
+import org.opensearch.knn.index.engine.TrainingConfigValidationInput;
+import org.opensearch.knn.index.engine.TrainingConfigValidationOutput;
 import org.opensearch.knn.index.VectorDataType;
 import org.opensearch.knn.indices.ModelDao;
 
 import java.io.IOException;
+import java.util.function.Function;
 
 /**
  * Request to train and serialize a model
@@ -283,6 +287,21 @@ public ActionRequestValidationException validate() {
             exception.addValidationError("Description exceeds limit of " + KNNConstants.MAX_MODEL_DESCRIPTION_LENGTH + " characters");
         }
 
+        KNNLibraryIndexingContext knnLibraryIndexingContext = knnMethodContext.getKnnEngine()
+            .getKNNLibraryIndexingContext(knnMethodContext, knnMethodConfigContext);
+        Function<TrainingConfigValidationInput, TrainingConfigValidationOutput> validateTrainingConfig = knnLibraryIndexingContext
+            .getTrainingConfigValidationSetup();
+        TrainingConfigValidationInput.TrainingConfigValidationInputBuilder inputBuilder = TrainingConfigValidationInput.builder();
+        TrainingConfigValidationOutput validation = validateTrainingConfig.apply(
+            inputBuilder.knnMethodConfigContext(knnMethodConfigContext).knnMethodContext(knnMethodContext).build()
+        );
+
+        // Check if ENCODER_PARAMETER_PQ_M is divisible by vector dimension
+        if (!validation.isValid()) {
+            exception = exception == null ? new ActionRequestValidationException() : exception;
+            exception.addValidationError("Training request ENCODER_PARAMETER_PQ_M is not divisible by vector dimensions");
+        }
+
         // Validate training index exists
         IndexMetadata indexMetadata = clusterService.state().metadata().index(trainingIndex);
         if (indexMetadata == null) {

src/main/java/org/opensearch/knn/training/TrainingJob.java

@@ -203,9 +203,7 @@ public void run() {
         } catch (Exception e) {
             logger.error("Failed to run training job for model \"" + modelId + "\": ", e);
             modelMetadata.setState(ModelState.FAILED);
-            modelMetadata.setError(
-                "Failed to execute training. May be caused by an invalid method definition or " + "not enough memory to perform training."
-            );
+            modelMetadata.setError("Failed to execute training. " + e.getMessage());
 
             KNNCounter.TRAINING_ERRORS.increment();
 

src/test/java/org/opensearch/knn/index/FaissIT.java

@@ -304,7 +304,7 @@ public void testEndToEnd_whenDoRadiusSearch_whenDistanceThreshold_whenMethodIsHN
 
         // training data needs to be at least equal to the number of centroids for PQ
         // which is 2^8 = 256. 8 because that's the only valid code_size for HNSWPQ
-        int trainingDataCount = 256;
+        int trainingDataCount = 1100;
 
         SpaceType spaceType = SpaceType.L2;
 
@@ -468,7 +468,7 @@ public void testEndToEnd_whenMethodIsHNSWPQ_thenSucceed() {
 
         // training data needs to be at least equal to the number of centroids for PQ
         // which is 2^8 = 256. 8 because thats the only valid code_size for HNSWPQ
-        int trainingDataCount = 256;
+        int trainingDataCount = 1100;
 
         SpaceType spaceType = SpaceType.L2;
 
@@ -736,7 +736,7 @@ public void testIVFSQFP16_whenIndexedAndQueried_thenSucceed() {
 
         // Add training data
         createBasicKnnIndex(trainingIndexName, trainingFieldName, dimension);
-        int trainingDataCount = 200;
+        int trainingDataCount = 1100;
         bulkIngestRandomVectors(trainingIndexName, trainingFieldName, trainingDataCount, dimension);
 
         XContentBuilder builder = XContentFactory.jsonBuilder()
@@ -960,7 +960,7 @@ public void testIVFSQFP16_whenIndexedWithOutOfFP16Range_thenThrowException() {
 
         // Add training data
         createBasicKnnIndex(trainingIndexName, trainingFieldName, dimension);
-        int trainingDataCount = 200;
+        int trainingDataCount = 1100;
         bulkIngestRandomVectors(trainingIndexName, trainingFieldName, trainingDataCount, dimension);
 
         XContentBuilder builder = XContentFactory.jsonBuilder()
@@ -1064,7 +1064,7 @@ public void testIVFSQFP16_whenClipToFp16isTrueAndIndexedWithOutOfFP16Range_thenS
 
         // Add training data
         createBasicKnnIndex(trainingIndexName, trainingFieldName, dimension);
-        int trainingDataCount = 200;
+        int trainingDataCount = 1100;
         bulkIngestRandomVectors(trainingIndexName, trainingFieldName, trainingDataCount, dimension);
 
         XContentBuilder builder = XContentFactory.jsonBuilder()
@@ -1144,7 +1144,7 @@ public void testEndToEnd_whenMethodIsHNSWPQAndHyperParametersNotSet_thenSucceed(
 
         // training data needs to be at least equal to the number of centroids for PQ
         // which is 2^8 = 256. 8 because thats the only valid code_size for HNSWPQ
-        int trainingDataCount = 256;
+        int trainingDataCount = 1100;
 
         SpaceType spaceType = SpaceType.L2;
 
@@ -1412,7 +1412,7 @@ public void testKNNQuery_withModelDifferentCombination_thenSuccess() throws Exce
 
         // Add training data
         createBasicKnnIndex(trainingIndexName, trainingFieldName, dimension);
-        int trainingDataCount = 200;
+        int trainingDataCount = 1100;
         bulkIngestRandomVectors(trainingIndexName, trainingFieldName, trainingDataCount, dimension);
 
         // Call train API - IVF with nlists = 1 is brute force, but will require training
@@ -1767,7 +1767,7 @@ public void testIVF_whenBinaryFormat_whenIVF_thenSuccess() {
 
         createKnnIndex(trainingIndexName, trainIndexMapping);
 
-        int trainingDataCount = 40;
+        int trainingDataCount = 1100;
         bulkIngestRandomBinaryVectors(trainingIndexName, trainingFieldName, trainingDataCount, dimension);
 
         XContentBuilder trainModelXContentBuilder = XContentFactory.jsonBuilder()

src/test/java/org/opensearch/knn/index/VectorDataTypeIT.java

@@ -603,7 +603,7 @@ public void testIVFByteVector_whenIndexedAndQueried_thenSucceed() {
             .toString();
         createKnnIndex(INDEX_NAME, trainIndexMapping);
 
-        int trainingDataCount = 100;
+        int trainingDataCount = 1100;
         bulkIngestRandomByteVectors(INDEX_NAME, FIELD_NAME, trainingDataCount, dimension);
 
         XContentBuilder trainModelXContentBuilder = XContentFactory.jsonBuilder()

src/test/java/org/opensearch/knn/integ/KNNScriptScoringIT.java

@@ -620,7 +620,7 @@ public void testKNNScriptScoreOnModelBasedIndex() throws Exception {
         int dimensions = randomIntBetween(2, 10);
         String trainMapping = createKnnIndexMapping(TRAIN_FIELD_PARAMETER, dimensions);
         createKnnIndex(TRAIN_INDEX_PARAMETER, trainMapping);
-        bulkIngestRandomVectors(TRAIN_INDEX_PARAMETER, TRAIN_FIELD_PARAMETER, dimensions * 3, dimensions);
+        bulkIngestRandomVectors(TRAIN_INDEX_PARAMETER, TRAIN_FIELD_PARAMETER, 1100, dimensions);
 
         XContentBuilder methodBuilder = XContentFactory.jsonBuilder()
             .startObject()

src/test/java/org/opensearch/knn/integ/ModeAndCompressionIT.java

@@ -52,7 +52,7 @@ public class ModeAndCompressionIT extends KNNRestTestCase {
 
     private static final String TRAINING_INDEX_NAME = "training_index";
     private static final String TRAINING_FIELD_NAME = "training_field";
-    private static final int TRAINING_VECS = 20;
+    private static final int TRAINING_VECS = 1100;
 
     private static final int DIMENSION = 16;
     private static final int NUM_DOCS = 20;