[V0][Bugfix] Fix parallel sampling performance regression when guided decoding is enabled

vllm/model_executor/guided_decoding/guidance_logits_processors.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+import copy
 import os
 from typing import Any
 
@@ -34,9 +35,24 @@ def __init__(
         self.grammar = grammar
         self.tokenizer = tokenizer
         self.tokenizer_name = tokenizer.name_or_path
+        self.ll_tokenizer = None
+        self.ll_matcher = None
+        self.bitmask = None
         self.new_sampling = False
         self.initialized = False
 
+    def clone(self) -> "GuidanceLogitsProcessor":
+        cloned = copy.copy(self)
+        if self.initialized:
+            cloned.ll_matcher = llguidance.LLMatcher(
+                self.ll_tokenizer,  # type: ignore[assignment]
+                self.grammar,
+                log_level=int(os.environ.get("LLGUIDANCE_LOG_LEVEL", "1")),
+            )
+            self.bitmask = llguidance.torch.allocate_token_bitmask(
+                1, self.ll_tokenizer.vocab_size)  # type: ignore[attr-defined]
+        return cloned
+
     def _initialize(self):
         if self.initialized:
             return
@@ -56,7 +72,7 @@ def _initialize(self):
 
         # create reusable bitmask
         self.bitmask = llguidance.torch.allocate_token_bitmask(
-            1, self.ll_tokenizer.vocab_size)
+            1, self.ll_tokenizer.vocab_size)  # type: ignore[attr-defined]
 
         self.initialized = True
 
@@ -70,15 +86,17 @@ def __call__(
         self._initialize()
 
         if self.new_sampling and len(input_ids) > 0:
-            self.ll_matcher.consume_token(input_ids[-1])
-            err = self.ll_matcher.get_error()
+            self.ll_matcher.consume_token(  # type: ignore[attr-defined]
+                input_ids[-1])
+            err = self.ll_matcher.get_error()  # type: ignore[attr-defined]
             if err:
                 logger.warning("Error in LLMatcher: %s", err)
 
         llguidance.torch.fill_next_token_bitmask(self.ll_matcher, self.bitmask,
                                                  0)
         llguidance.torch.apply_token_bitmask_inplace(
-            scores, self.bitmask.to(scores.device))
+            scores,
+            self.bitmask.to(scores.device))  # type: ignore[attr-defined]
 
         self.new_sampling = True
 

vllm/model_executor/guided_decoding/outlines_logits_processors.py

@@ -56,6 +56,12 @@ def __init__(self, guide: Guide, reasoner: Optional[ReasoningParser]):
         self._fsm_state: defaultdict[int, Union[int,
                                                 CFGState]] = defaultdict(int)
 
+    def clone(self) -> "BaseLogitsProcessor":
+        cloned = copy.copy(self)
+        cloned._guide = self._guide.copy()
+        cloned._fsm_state = copy.deepcopy(self._fsm_state)
+        return cloned
+
     def __call__(self, input_ids: list[int],
                  scores: torch.Tensor) -> torch.Tensor:
         """Use the FSM to bias the logits before sampling the next token."""
@@ -218,6 +224,12 @@ def __init__(self, cfg: str, tokenizer: PreTrainedTokenizerBase,
                          reasoner)
         self._guide = self._guide.copy()
 
+    def clone(self) -> "CFGLogitsProcessor":
+        cloned = copy.copy(self)
+        cloned._fsm_state = copy.deepcopy(self._fsm_state)
+        cloned._guide = self._guide.copy()
+        return cloned
+
 
 @lru_cache(maxsize=32)
 def _adapt_tokenizer(tokenizer: PreTrainedTokenizerBase):

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -302,8 +302,9 @@ class XGrammarLogitsProcessor:
     prefilled: bool = field(default=False)
 
     def __post_init__(self):
-        self.tokenizer_info = self.config.tokenizer_info(
-            self.config.tokenizer_data)
+        if self.tokenizer_info is None:
+            self.tokenizer_info = self.config.tokenizer_info(
+                self.config.tokenizer_data)
 
     def __getstate__(self) -> dict[str, Any]:
         return {'config': self.config, 'reasoner': self.reasoner}
@@ -400,7 +401,8 @@ def __call__(self, input_ids: list[int],
     def clone(self) -> XGrammarLogitsProcessor:
         """Create a new instance with shared compiled grammar
           but separate state"""
-        new_processor = XGrammarLogitsProcessor(self.config, self.reasoner)
+        new_processor = XGrammarLogitsProcessor(self.config, self.reasoner,
+                                                None, self.tokenizer_info)
 
         # Share the compiled grammar context (immutable after compilation)
         new_processor.ctx = self.ctx

vllm/sequence.py

@@ -1494,7 +1494,7 @@ def add_request(request_id: str, engine, params, **kwargs):
         for i in range(original_params.n):
             request_id_i = f"{request_id}_parallel_sample_{i}"
             group.seq_id_to_index[request_id_i] = i
-            params = copy.deepcopy(original_params)
+            params = params.clone()
             params.n = 1
             if params.seed is not None:
                 params.seed += i