Revert "feat: reduce constrained-decoding overhead in TP" (#16845)

python/sglang/srt/sampling/sampling_batch_info.py

@@ -201,11 +201,7 @@ class SamplingBatchInfo:
             return
 
         # Find a grammar from the list
-        first_grammar = next((grammar for grammar in self.grammars if grammar), None)
-        if first_grammar is None:
-            self.vocab_mask = None
-            self.apply_mask_func = None
-            return
+        first_grammar = next(grammar for grammar in self.grammars if grammar)
 
         # TODO(lianmin): Maybe we can reuse the existing mask?
         self.vocab_mask = first_grammar.allocate_vocab_mask(
@@ -236,7 +232,7 @@ class SamplingBatchInfo:
         else:
             self.acc_linear_penalties = None
 
-    def apply_logits_bias(self, logits: torch.Tensor, apply_vocab_mask: bool = True):
+    def apply_logits_bias(self, logits: torch.Tensor):
         if self.acc_linear_penalties is not None:
             # Used in the overlap mode
             logits.add_(self.acc_linear_penalties)
@@ -245,7 +241,7 @@ class SamplingBatchInfo:
             # Used in the non-overlap mode
             self.penalizer_orchestrator.apply(logits)
 
-        if apply_vocab_mask and self.vocab_mask is not None:
+        if self.vocab_mask is not None:
             self.apply_mask_func(logits=logits, vocab_mask=self.vocab_mask)
 
         if self.logit_bias is not None:

python/sglang/srt/sampling/sampling_params.py

@@ -102,19 +102,6 @@ class SamplingParams:
         if self.top_k == -1:
             self.top_k = TOP_K_ALL  # whole vocabulary
 
-    @property
-    def has_grammar_constraint(self) -> bool:
-        """
-        Helper property to check if any grammar constraint
-        exists for these SamplingParams.
-        """
-        return (
-            self.json_schema is not None
-            or self.regex is not None
-            or self.ebnf is not None
-            or self.structural_tag is not None
-        )
-
     def verify(self, vocab_size):
         if self.temperature < 0.0:
             raise ValueError(