Fix the perf regression due to additional_stop_token_ids (#1773)

python/sglang/srt/sampling/penaltylib/penalizers/min_new_tokens.py

@@ -31,9 +31,12 @@ class BatchedMinNewTokensPenalizer(_BatchedPenalizer):
         padded_stop_token_ids = torch.nn.utils.rnn.pad_sequence(
             sequences=[
                 torch.tensor(
-                    data=list(
-                        req.sampling_params.stop_token_ids
-                        | {req.tokenizer.eos_token_id}
+                    data=(
+                        list(
+                            (req.sampling_params.stop_token_ids or set())
+                            | (req.tokenizer.additional_stop_token_ids or set())
+                            | {req.tokenizer.eos_token_id}
+                        )
                     ),
                     dtype=torch.int64,
                     device=self.orchestrator.device,

python/sglang/srt/sampling/sampling_params.py

@@ -50,10 +50,10 @@ class SamplingParams:
         self.presence_penalty = presence_penalty
         self.repetition_penalty = repetition_penalty
         self.stop_strs = stop
-        if stop_token_ids is None:
-            self.stop_token_ids = set()
-        else:
+        if stop_token_ids:
             self.stop_token_ids = set(stop_token_ids)
+        else:
+            self.stop_token_ids = None
         self.max_new_tokens = max_new_tokens
         self.min_new_tokens = min_new_tokens
         self.ignore_eos = ignore_eos
@@ -134,10 +134,6 @@ class SamplingParams:
                     stop_str_max_len = max(stop_str_max_len, len(stop_str))
             self.stop_str_max_len = stop_str_max_len
 
-        # Process stop token ids
-        if tokenizer and tokenizer.additional_stop_token_ids:
-            self.stop_token_ids.update(tokenizer.additional_stop_token_ids)
-
     def to_srt_kwargs(self):
         return {
             "max_new_tokens": self.max_new_tokens,