[ML] Fix for Deberta tokenizer when input sequence exceeds 512 tokens (elastic#117595)

* Add test and fix

* Update docs/changelog/117595.yaml

* Remove test which wasn't working

(cherry picked from commit 433a00c)

Author: maxhniebergall

docs/changelog/117595.yaml

@@ -0,0 +1,5 @@
+pr: 117595
+summary: Fix for Deberta tokenizer when input sequence exceeds 512 tokens
+area: Machine Learning
+type: bug
+issues: []

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

@@ -331,6 +331,29 @@ public List<TokenizationResult.Tokens> tokenize(String seq1, String seq2, Tokeni
                     tokenIdsSeq2 = tokenIdsSeq2.subList(0, maxSequenceLength() - extraTokens - tokenIdsSeq1.size());
                     tokenPositionMapSeq2 = tokenPositionMapSeq2.subList(0, maxSequenceLength() - extraTokens - tokenIdsSeq1.size());
                 }
+                case BALANCED -> {
+                    isTruncated = true;
+                    int firstSequenceLength = 0;
+
+                    if (tokenIdsSeq2.size() > (maxSequenceLength() - getNumExtraTokensForSeqPair()) / 2) {
+                        firstSequenceLength = min(tokenIdsSeq1.size(), (maxSequenceLength() - getNumExtraTokensForSeqPair()) / 2);
+                    } else {
+                        firstSequenceLength = min(
+                            tokenIdsSeq1.size(),
+                            maxSequenceLength() - tokenIdsSeq2.size() - getNumExtraTokensForSeqPair()
+                        );
+                    }
+                    int secondSequenceLength = min(
+                        tokenIdsSeq2.size(),
+                        maxSequenceLength() - firstSequenceLength - getNumExtraTokensForSeqPair()
+                    );
+
+                    tokenIdsSeq1 = tokenIdsSeq1.subList(0, firstSequenceLength);
+                    tokenPositionMapSeq1 = tokenPositionMapSeq1.subList(0, firstSequenceLength);
+
+                    tokenIdsSeq2 = tokenIdsSeq2.subList(0, secondSequenceLength);
+                    tokenPositionMapSeq2 = tokenPositionMapSeq2.subList(0, secondSequenceLength);
+                }
                 case NONE -> throw ExceptionsHelper.badRequestException(
                     "Input too large. The tokenized input length [{}] exceeds the maximum sequence length [{}]",
                     numTokens,

x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/inference/nlp/TextSimilarityProcessorTests.java

@@ -10,18 +10,22 @@
 import org.elasticsearch.test.ESTestCase;
 import org.elasticsearch.xpack.core.ml.inference.results.TextSimilarityInferenceResults;
 import org.elasticsearch.xpack.core.ml.inference.trainedmodel.BertTokenization;
+import org.elasticsearch.xpack.core.ml.inference.trainedmodel.DebertaV2Tokenization;
 import org.elasticsearch.xpack.core.ml.inference.trainedmodel.TextSimilarityConfig;
 import org.elasticsearch.xpack.core.ml.inference.trainedmodel.Tokenization;
 import org.elasticsearch.xpack.core.ml.inference.trainedmodel.VocabularyConfig;
 import org.elasticsearch.xpack.ml.inference.nlp.tokenizers.BertTokenizationResult;
 import org.elasticsearch.xpack.ml.inference.nlp.tokenizers.BertTokenizer;
+import org.elasticsearch.xpack.ml.inference.nlp.tokenizers.DebertaV2Tokenizer;
 import org.elasticsearch.xpack.ml.inference.nlp.tokenizers.TokenizationResult;
 import org.elasticsearch.xpack.ml.inference.pytorch.results.PyTorchInferenceResult;
 
 import java.io.IOException;
 import java.util.List;
 
 import static org.elasticsearch.xpack.ml.inference.nlp.tokenizers.BertTokenizerTests.TEST_CASED_VOCAB;
+import static org.elasticsearch.xpack.ml.inference.nlp.tokenizers.DebertaV2TokenizerTests.TEST_CASE_SCORES;
+import static org.elasticsearch.xpack.ml.inference.nlp.tokenizers.DebertaV2TokenizerTests.TEST_CASE_VOCAB;
 import static org.hamcrest.Matchers.closeTo;
 import static org.hamcrest.Matchers.equalTo;
 import static org.hamcrest.Matchers.is;
@@ -62,6 +66,33 @@ public void testProcessor() throws IOException {
         assertThat(result.predictedValue(), closeTo(42, 1e-6));
     }
 
+    public void testBalancedTruncationWithLongInput() throws IOException {
+        String question = "Is Elasticsearch scalable?";
+        StringBuilder longInputBuilder = new StringBuilder();
+        for (int i = 0; i < 1000; i++) {
+            longInputBuilder.append(TEST_CASE_VOCAB.get(randomIntBetween(0, TEST_CASE_VOCAB.size() - 1))).append(i).append(" ");
+        }
+        String longInput = longInputBuilder.toString().trim();
+
+        DebertaV2Tokenization tokenization = new DebertaV2Tokenization(false, true, null, Tokenization.Truncate.BALANCED, -1);
+        DebertaV2Tokenizer tokenizer = DebertaV2Tokenizer.builder(TEST_CASE_VOCAB, TEST_CASE_SCORES, tokenization).build();
+        TextSimilarityConfig textSimilarityConfig = new TextSimilarityConfig(
+            question,
+            new VocabularyConfig(""),
+            tokenization,
+            "result",
+            TextSimilarityConfig.SpanScoreFunction.MAX
+        );
+        TextSimilarityProcessor processor = new TextSimilarityProcessor(tokenizer);
+        TokenizationResult tokenizationResult = processor.getRequestBuilder(textSimilarityConfig)
+            .buildRequest(List.of(longInput), "1", Tokenization.Truncate.BALANCED, -1, null)
+            .tokenization();
+
+        // Assert that the tokenization result is as expected
+        assertThat(tokenizationResult.anyTruncated(), is(true));
+        assertThat(tokenizationResult.getTokenization(0).tokenIds().length, equalTo(512));
+    }
+
     public void testResultFunctions() {
         BertTokenization tokenization = new BertTokenization(false, true, 384, Tokenization.Truncate.NONE, 128);
         BertTokenizer tokenizer = BertTokenizer.builder(TEST_CASED_VOCAB, tokenization).build();

x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/inference/nlp/tokenizers/DebertaV2TokenizerTests.java

@@ -23,7 +23,7 @@
 
 public class DebertaV2TokenizerTests extends ESTestCase {
 
-    private static final List<String> TEST_CASE_VOCAB = List.of(
+    public static final List<String> TEST_CASE_VOCAB = List.of(
         DebertaV2Tokenizer.CLASS_TOKEN,
         DebertaV2Tokenizer.PAD_TOKEN,
         DebertaV2Tokenizer.SEPARATOR_TOKEN,
@@ -48,7 +48,7 @@ public class DebertaV2TokenizerTests extends ESTestCase {
         "<0xAD>",
         "▁"
     );
-    private static final List<Double> TEST_CASE_SCORES = List.of(
+    public static final List<Double> TEST_CASE_SCORES = List.of(
         0.0,
         0.0,
         0.0,