fix: lint error

Signed-off-by: Aaron Pham <[email protected]>

tests/model_executor/test_guided_processors.py

@@ -36,7 +36,8 @@ def test_guided_logits_processors(sample_regex, sample_json_schema):
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("backend", ["outlines", "lm-format-enforcer", "xgrammar"])
+@pytest.mark.parametrize("backend",
+                         ["outlines", "lm-format-enforcer", "xgrammar"])
 async def test_guided_logits_processor_black_box(backend: str, sample_regex,
                                                  sample_json_schema):
     tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -1,10 +1,11 @@
+# noqa: UP007
 from __future__ import annotations
 
-import json, torch
-
+import json
+import torch
 from transformers import PreTrainedTokenizerFast
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Dict, Any, Optional, List
+from typing import TYPE_CHECKING, Dict, Optional, List, Any
 
 try:
     import xgrammar as xgr
@@ -23,7 +24,7 @@ def get_local_xgrammar_guided_decoding_logits_processor(
         guided_params: GuidedDecodingParams,
         tokenizer: PreTrainedTokenizer,
         model_config: ModelConfig,
-        max_threads=8):
+        max_threads: int = 8):
     config = GrammarConfig.from_guided_params(guided_params=guided_params,
                                               model_config=model_config,
                                               tokenizer=tokenizer,
@@ -57,10 +58,9 @@ def from_guided_params(cls,
                                              key=lambda x: x[1])
             ]
         except AttributeError as e:
-            msg = (
-                f"Cannot get the vocabulary of the tokenizer {type(tokenizer)}. The tokenizer "
-                "should have a get_vocab method.")
-            raise ValueError(msg) from e
+            raise ValueError(
+                f"Cannot get the vocabulary of the tokenizer {type(tokenizer)}. The tokenizer should have a get_vocab method."
+            ) from e
 
         stop_token_ids = None
         backend_str = xgr.VocabType.RAW
@@ -79,11 +79,6 @@ def from_guided_params(cls,
                         tokenizer,
                         "eos_token_id") and tokenizer.eos_token_id is not None:
                     stop_token_ids = [tokenizer.eos_token_id]
-                else:
-                    logger.warning(
-                        "When constructing TokenizerInfo from a huggingface tokenizer, "
-                        "stop_token_ids is neither provided by user nor found from the tokenizer. "
-                        "It will be automatically detected.")
 
         if guided_params.json:
             if not isinstance(guided_params.json, str):
@@ -124,8 +119,8 @@ class XGrammarLogitsProcessor:
     ctx: Optional[xgr.CompiledGrammar] = None
     matchers: List[xgr.GrammarMatcher] = field(default_factory=list)
     batch_size: int = 1
-    token_bitmask: Optional[torch.Tensor]  = None
-    prefilled: boolean = False
+    token_bitmask: torch.Tensor = None
+    prefilled: bool = False
 
     def __getstate__(self) -> Dict[str, Any]:
         return {'config': self.config}
@@ -152,7 +147,9 @@ def _ensure_ctx(self):
 
     def __call__(self, input_ids: List[int],
                  scores: torch.Tensor) -> torch.Tensor:
-        if self.ctx is None: self._ensure_ctx()
+        if self.ctx is None:
+            self._ensure_ctx()
+
         if len(self.matchers) == 0:
             self.matchers = [
                 xgr.GrammarMatcher(self.ctx) for _ in range(self.batch_size)
@@ -174,9 +171,11 @@ def __call__(self, input_ids: List[int],
                 matcher.fill_next_token_bitmask(self.token_bitmask, i)
 
         device_type = scores.device.type
-        if device_type != "cuda": scores = scores.to("cpu")
+        if device_type != "cuda":
+            scores = scores.to("cpu")
         xgr.apply_token_bitmask_inplace(scores,
                                         self.token_bitmask.to(scores.device))
-        if device_type != "cuda": scores = scores.to(device_type)
+        if device_type != "cuda":
+            scores = scores.to(device_type)
 
         return scores