Revert "feat: add thinking_budget (#6089)" (#6181)

python/sglang/srt/model_executor/model_runner.py

@@ -1145,9 +1145,7 @@ class ModelRunner:
                 [self.sample(values, forward_batch) for values in logits_output],
                 axis=-1,
             )
-        sampling_info = forward_batch.sampling_info
-        if sampling_info.thinking_budgets is not None:
-            sampling_info.apply_thinking_budgets(logits_output.next_token_logits)
+
         self._preprocess_logits(logits_output, forward_batch.sampling_info)
 
         # Sample the next tokens
@@ -1158,8 +1156,6 @@ class ModelRunner:
             forward_batch.top_logprobs_nums,
             forward_batch.token_ids_logprobs,
         )
-        if sampling_info.thinking_budgets is not None:
-            sampling_info.update_thinking_budgets(next_token_ids)
         return next_token_ids
 
     @property

python/sglang/srt/openai_api/adapter.py

@@ -529,7 +529,6 @@ def v1_generate_request(
                 "temperature": request.temperature,
                 "max_new_tokens": request.max_tokens,
                 "min_new_tokens": request.min_tokens,
-                "thinking_budget": request.thinking_budget,
                 "stop": request.stop,
                 "stop_token_ids": request.stop_token_ids,
                 "top_p": request.top_p,
@@ -1102,7 +1101,6 @@ def v1_chat_generate_request(
             "temperature": request.temperature,
             "max_new_tokens": request.max_tokens or request.max_completion_tokens,
             "min_new_tokens": request.min_tokens,
-            "thinking_budget": request.thinking_budget,
             "stop": stop,
             "stop_token_ids": request.stop_token_ids,
             "top_p": request.top_p,

python/sglang/srt/openai_api/protocol.py

@@ -172,7 +172,6 @@ class CompletionRequest(BaseModel):
     top_k: int = -1
     min_p: float = 0.0
     min_tokens: int = 0
-    thinking_budget: Optional[int] = None
     json_schema: Optional[str] = None
     regex: Optional[str] = None
     ebnf: Optional[str] = None
@@ -351,13 +350,6 @@ class ChatCompletionRequest(BaseModel):
         description="The maximum number of completion tokens for a chat completion request, "
         "including visible output tokens and reasoning tokens. Input tokens are not included. ",
     )
-    thinking_budget: Optional[int] = Field(
-        default=None,
-        description="The maximum number of reasoning tokens that can be generated for a request. "
-        "This setting of does not affect the thinking process of models. "
-        "If the number of tokens generated by the model's thinking process exceeds thinking_budget, "
-        "the reasoning content will be truncated and the final response content will be generated immediately.",
-    )
     n: int = 1
     presence_penalty: float = 0.0
     response_format: Optional[Union[ResponseFormat, StructuralTagResponseFormat]] = None

python/sglang/srt/reasoning_parser.py

@@ -32,7 +32,7 @@ class BaseReasoningFormatDetector:
         One-time parsing: Detects and parses reasoning sections in the provided text.
         Returns both reasoning content and normal text separately.
         """
-        text = text.replace(self.think_start_token, "")
+        text = text.replace(self.think_start_token, "").strip()
         if self.think_end_token not in text:
             # Assume reasoning was truncated before `</think>` token
             return StreamingParseResult(reasoning_text=text)
@@ -73,7 +73,7 @@ class BaseReasoningFormatDetector:
             normal_text = current_text[end_idx + len(self.think_end_token) :]
 
             return StreamingParseResult(
-                normal_text=normal_text, reasoning_text=reasoning_text
+                normal_text=normal_text, reasoning_text=reasoning_text.rstrip()
             )
 
         # Continue with reasoning content

python/sglang/srt/sampling/sampling_batch_info.py

@@ -30,13 +30,8 @@ class SamplingBatchInfo:
     # Whether any request needs min_p sampling
     need_min_p_sampling: bool
 
-    # Use thinking_budget to truncate thinking
-    num_thinking_tokens: Optional[torch.Tensor] = None
-    think_end_ids: Optional[torch.Tensor] = None
-    thinking_budgets: Optional[torch.Tensor] = None
-
     # Masking tensors for grammar-guided structured outputs
-    vocab_size: int = 0
+    vocab_size: int
     grammars: Optional[List] = None
     vocab_mask: Optional[torch.Tensor] = None
     apply_mask_func: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None
@@ -81,22 +76,7 @@ class SamplingBatchInfo:
         min_ps = torch.tensor(
             [r.sampling_params.min_p for r in reqs], dtype=torch.float
         ).to(device, non_blocking=True)
-        if any(hasattr(r.tokenizer, "think_end_id") for r in reqs):
-            think_end_ids = torch.tensor(
-                [getattr(r.tokenizer, "think_end_id", -1) for r in reqs],
-                dtype=torch.int64,
-            ).to(device, non_blocking=True)
-            num_thinking_tokens = torch.tensor([0 for _ in reqs], dtype=torch.int64).to(
-                device, non_blocking=True
-            )
-            thinking_budgets = torch.tensor(
-                [r.sampling_params.thinking_budget or -1 for r in reqs],
-                dtype=torch.int64,
-            ).to(device, non_blocking=True)
-        else:
-            think_end_ids = None
-            num_thinking_tokens = None
-            thinking_budgets = None
+
         # Check if any request has custom logit processor
         has_custom_logit_processor = (
             batch.enable_custom_logit_processor  # check the flag first.
@@ -152,9 +132,6 @@ class SamplingBatchInfo:
             top_ps=top_ps,
             top_ks=top_ks,
             min_ps=min_ps,
-            think_end_ids=think_end_ids,
-            num_thinking_tokens=num_thinking_tokens,
-            thinking_budgets=thinking_budgets,
             is_all_greedy=all(r.sampling_params.top_k <= 1 for r in reqs),
             need_min_p_sampling=any(r.sampling_params.min_p > 0 for r in reqs),
             vocab_size=vocab_size,
@@ -169,35 +146,6 @@ class SamplingBatchInfo:
     def __len__(self):
         return len(self.temperatures)
 
-    def apply_thinking_budgets(self, next_token_logits: torch.Tensor):
-        has_budget = self.thinking_budgets > 0
-        if not has_budget.any():
-            return
-        torch.where(
-            has_budget,
-            self.num_thinking_tokens + 1,
-            self.num_thinking_tokens,
-            out=self.num_thinking_tokens,
-        )
-        should_stop = has_budget & (
-            self.num_thinking_tokens - 1 > self.thinking_budgets
-        )
-        next_token_logits.masked_fill_(should_stop.unsqueeze(0), float("-inf"))
-        batch_indices = torch.nonzero(should_stop, as_tuple=True)[0]
-        if len(batch_indices) > 0:
-            end_token_indices = self.think_end_ids[batch_indices]
-            next_token_logits[batch_indices, end_token_indices] = 0.0
-
-    def update_thinking_budgets(self, next_token_ids: torch.Tensor):
-        if not torch.any(self.thinking_budgets > 0):
-            return
-        torch.where(
-            next_token_ids == self.think_end_ids,
-            torch.tensor(-1, device=self.thinking_budgets.device),
-            self.thinking_budgets,
-            out=self.thinking_budgets,
-        )
-
     def update_regex_vocab_mask(self):
         if not self.grammars:
             self.vocab_mask = None

python/sglang/srt/sampling/sampling_params.py

@@ -30,7 +30,6 @@ class SamplingParams:
     def __init__(
         self,
         max_new_tokens: int = 128,
-        thinking_budget: Optional[int] = None,
         stop: Optional[Union[str, List[str]]] = None,
         stop_token_ids: Optional[List[int]] = None,
         temperature: float = 1.0,
@@ -58,7 +57,6 @@ class SamplingParams:
             self.stop_token_ids = set(stop_token_ids)
         else:
             self.stop_token_ids = None
-        self.thinking_budget = thinking_budget
         self.temperature = temperature
         self.top_p = top_p
         self.top_k = top_k