[Core] Upgrade to xgrammar 0.1.18, add cache size limit (#16283)

Signed-off-by: Russell Bryant <rbryant@redhat.com>

requirements/common.txt

@@ -22,7 +22,7 @@ lm-format-enforcer >= 0.10.11, < 0.11
 llguidance >= 0.7.9, < 0.8.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64"
 outlines == 0.1.11
 lark == 1.2.2
-xgrammar == 0.1.17; platform_machine == "x86_64" or platform_machine == "aarch64"
+xgrammar == 0.1.18; platform_machine == "x86_64" or platform_machine == "aarch64"
 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/envs.py

@@ -106,6 +106,7 @@ if TYPE_CHECKING:
     VLLM_TPU_DISABLE_TOPK_TOPP_OPTIMIZATION: bool = False
     VLLM_TPU_BUCKET_PADDING_GAP: int = 0
     VLLM_USE_DEEP_GEMM: bool = False
+    VLLM_XGRAMMAR_CACHE_MB: int = 0
 
 
 def get_default_cache_root():
@@ -697,6 +698,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Allow use of DeepGemm kernels for fused moe ops.
     "VLLM_USE_DEEP_GEMM":
     lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "0"))),
+
+    # Control the cache sized used by the xgrammar compiler. The default
+    # of 512 MB should be enough for roughly 1000 JSON schemas.
+    # It can be changed with this variable if needed for some reason.
+    "VLLM_XGRAMMAR_CACHE_MB":
+    lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
 }
 
 # end-env-vars-definition

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -10,6 +10,7 @@ from typing import TYPE_CHECKING, Any, List
 
 import torch
 
+import vllm.envs
 from vllm.logger import init_logger
 
 try:
@@ -131,8 +132,13 @@ class GrammarCompilerCache:
                 encoded_vocab=config_data.encoded_vocab,
                 metadata=config_data.metadata,
             )
+            cache_size = vllm.envs.VLLM_XGRAMMAR_CACHE_MB * 1024 * 1024
             cls._cache[cache_key] = xgr.GrammarCompiler(
-                tokenizer_info, max_threads=config.max_threads)
+                tokenizer_info,
+                max_threads=config.max_threads,
+                cache_enabled=True,
+                cache_limit_bytes=cache_size,
+            )
 
         return cls._cache[cache_key]
 

vllm/v1/structured_output/backend_xgrammar.py

@@ -5,6 +5,7 @@ from typing import TYPE_CHECKING
 
 import torch
 
+import vllm.envs
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
@@ -76,7 +77,12 @@ class XgrammarBackend(StructuredOutputBackend):
                 tokenizer,
                 vocab_size=self.vocab_size,
             )
-        self.compiler = xgr.GrammarCompiler(tokenizer_info, max_threads=8)
+        self.compiler = xgr.GrammarCompiler(
+            tokenizer_info,
+            max_threads=8,
+            cache_enabled=True,
+            cache_limit_bytes=vllm.envs.VLLM_XGRAMMAR_CACHE_MB * 1024 * 1024,
+        )
 
     def compile_grammar(self, request_type: StructuredOutputOptions,
                         grammar_spec: str) -> StructuredOutputGrammar: