[ML] Record errors relating to the output of a model as conflict (#125135)

Co-authored-by: Pat Whelan <[email protected]>

Author: davidkyle

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/nlp/QuestionAnsweringProcessor.java

@@ -125,7 +125,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
             if (pyTorchResult.getInferenceResult().length % 2 != 0) {
                 throw new ElasticsearchStatusException(
                     "question answering result has invalid dimension, number of dimensions must be a multiple of 2 found [{}]",
-                    RestStatus.INTERNAL_SERVER_ERROR,
+                    RestStatus.CONFLICT,
                     pyTorchResult.getInferenceResult().length
                 );
             }
@@ -138,7 +138,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
             if (numberOfSpans != tokensList.size()) {
                 throw new ElasticsearchStatusException(
                     "question answering result has invalid dimensions; the number of spans [{}] does not match batched token size [{}]",
-                    RestStatus.INTERNAL_SERVER_ERROR,
+                    RestStatus.CONFLICT,
                     numberOfSpans,
                     tokensList.size()
                 );
@@ -153,7 +153,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
                 if (starts.length != ends.length) {
                     throw new ElasticsearchStatusException(
                         "question answering result has invalid dimensions; start positions [{}] must equal potential end [{}]",
-                        RestStatus.INTERNAL_SERVER_ERROR,
+                        RestStatus.CONFLICT,
                         starts.length,
                         ends.length
                     );
@@ -222,7 +222,7 @@ static void topScores(
         if (start.length != end.length) {
             throw new ElasticsearchStatusException(
                 "question answering result has invalid dimensions; possible start tokens [{}] must equal possible end tokens [{}]",
-                RestStatus.INTERNAL_SERVER_ERROR,
+                RestStatus.CONFLICT,
                 start.length,
                 end.length
             );

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/nlp/TextClassificationProcessor.java

@@ -99,7 +99,7 @@ static InferenceResults processResult(
             if (result.length != labels.size()) {
                 throw new ElasticsearchStatusException(
                     "Expected exactly [{}] values in text classification result; got [{}]",
-                    RestStatus.INTERNAL_SERVER_ERROR,
+                    RestStatus.CONFLICT,
                     labels.size(),
                     result.length
                 );
@@ -108,7 +108,7 @@ static InferenceResults processResult(
         Map<Integer, List<TokenizationResult.Tokens>> windowedSeq = tokenization.getTokensBySequenceId();
         // TODO adjust logic when batch is allowed
         if (windowedSeq.size() > 1) {
-            throw new ElasticsearchStatusException("Unexpected batch input for text classification", RestStatus.INTERNAL_SERVER_ERROR);
+            throw new ElasticsearchStatusException("Unexpected batch input for text classification", RestStatus.CONFLICT);
         }
         double[] normalizedScores = new double[labels.size()];
         for (int i = 0; i < pyTorchResult.getInferenceResult()[0].length; i++) {

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/nlp/TextSimilarityProcessor.java

@@ -99,7 +99,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
                 if (result.length != 1) {
                     throw new ElasticsearchStatusException(
                         "Expected exactly [1] value in text_similarity result; got [{}]",
-                        RestStatus.INTERNAL_SERVER_ERROR,
+                        RestStatus.CONFLICT,
                         result.length
                     );
                 }

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/nlp/ZeroShotClassificationProcessor.java

@@ -147,7 +147,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
             if (pyTorchResult.getInferenceResult()[0].length != labels.length) {
                 throw new ElasticsearchStatusException(
                     "Expected exactly [{}] values in zero shot classification result; got [{}]",
-                    RestStatus.INTERNAL_SERVER_ERROR,
+                    RestStatus.CONFLICT,
                     labels.length,
                     pyTorchResult.getInferenceResult().length
                 );
@@ -160,7 +160,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
                     if (vals.length != 3) {
                         throw new ElasticsearchStatusException(
                             "Expected exactly [{}] values in inner zero shot classification result; got [{}]",
-                            RestStatus.INTERNAL_SERVER_ERROR,
+                            RestStatus.CONFLICT,
                             3,
                             vals.length
                         );
@@ -177,7 +177,7 @@ public InferenceResults processResult(TokenizationResult tokenization, PyTorchIn
                     if (vals.length != 3) {
                         throw new ElasticsearchStatusException(
                             "Expected exactly [{}] values in inner zero shot classification result; got [{}]",
-                            RestStatus.INTERNAL_SERVER_ERROR,
+                            RestStatus.CONFLICT,
                             3,
                             vals.length
                         );