[Core] choice-based structured output with xgrammar (#12632)

requirements-common.txt

@@ -20,7 +20,7 @@ tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer >= 0.10.9, < 0.11
 outlines == 0.1.11
 lark == 1.2.2 
-xgrammar >= 0.1.6; platform_machine == "x86_64"
+xgrammar >= 0.1.11; platform_machine == "x86_64"
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
 partial-json-parser # used for parsing partial JSON outputs

vllm/model_executor/guided_decoding/__init__.py

@@ -49,11 +49,10 @@ def maybe_backend_fallback(
                            "Falling back to use outlines instead.")
             guided_params.backend = "outlines"
 
-        # xgrammar doesn't support regex or choice, fallback to outlines
-        if guided_params.regex is not None or guided_params.choice is not None:
-            logger.warning(
-                "xgrammar only supports json or grammar guided decoding. "
-                "Falling back to use outlines instead.")
+        # xgrammar doesn't support regex, fallback to outlines
+        if guided_params.regex is not None:
+            logger.warning("xgrammar does not support regex guided decoding. "
+                           "Falling back to use outlines instead.")
             guided_params.backend = "outlines"
 
         # xgrammar doesn't support some JSON schema features

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -5,8 +5,9 @@ from __future__ import annotations
 
 import copy
 import json
+import re
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, List
 
 import torch
 from transformers import PreTrainedTokenizerFast
@@ -228,11 +229,39 @@ class GrammarConfig:
                 max_threads=max_threads,
                 tokenizer_data=tokenizer_data,
             )
+        elif guided_params.choice:
+            choice_str = GrammarConfig.choice_as_grammar(guided_params.choice)
+            try:
+                xgr.Grammar.from_ebnf(choice_str)
+            except RuntimeError as err:
+                raise ValueError(str(err)) from err
+
+            return cls(
+                grammar_str=choice_str,
+                vocab_size=model_config.hf_text_config.vocab_size,
+                tokenizer_hash=tokenizer_hash,
+                max_threads=max_threads,
+                tokenizer_data=tokenizer_data,
+            )
         else:
             raise ValueError(
                 "Currently only support JSON and EBNF grammar mode for xgrammar"
             )
 
+    @staticmethod
+    def escape_ebnf_string(s: str) -> str:
+        """Escape special characters in a EBNF string."""
+        # Escape double quotes and backslashes
+        return re.sub(r'(["\\])', r'\\\1', s)
+
+    @staticmethod
+    def choice_as_grammar(choice: List[str] | None) -> str:
+        if choice is None:
+            raise ValueError("Choice is not set")
+        escaped_choices = (GrammarConfig.escape_ebnf_string(c) for c in choice)
+        grammar = ('root ::= ' + ' | '.join(f'"{c}"' for c in escaped_choices))
+        return grammar
+
 
 @dataclass
 class XGrammarLogitsProcessor: