[Bugfix] Align thinking_budget behavior with ERNIE reasoning flow (#6934)

* [Bugfix] Align thinking_budget behavior with ERNIE reasoning flow

* [Docs] Fix thinking_budget markdown formatting

* [Test] Align ernie thinking budget test with process_request_dict

docs/features/thinking_budget.md

@@ -2,7 +2,9 @@
 
 ## Overview
 
-`ThinkingBudgetLogitsProcessor` limits the number of tokens generated inside the `<think> ... </think>` segment. When the budget is reached, it forces a line break token and then the `</think>` token to terminate the thinking section.
+`ThinkingBudgetLogitsProcessor` limits the number of tokens generated inside the `<think> ... </think>`
+segment. When the budget is reached, it terminates thinking by forcing `</think>`. If
+`think_stop_sentence` is configured, it forces the custom sentence first and then `</think>`.
 
 ## When to Use
 
@@ -11,19 +13,22 @@
 
 ## How It Works
 
-1. **CPU precompute (DataProcessor)**: when a request includes `thinking_budget`, the prompt token ids are scanned to determine whether thinking has started, whether it already ended, and how many tokens are already inside the thinking section.
+1. **Request-side precompute (DataProcessor)**: when a request includes `thinking_budget`, the prompt token ids are scanned to determine whether thinking has started, whether it already ended, and how many tokens are already inside the thinking section.
 2. **Per-step update**: during decoding, the processor tracks `last_token_id` and `tokens_after_start`.
-3. **Budget enforcement**: once the budget is reached, it forces a line break and then the thinking end token.
+3. **Budget enforcement**: once the budget is reached, it forces `</think>` directly. If `think_stop_sentence`
+   is configured, it forces that sentence first and then `</think>`.
 
 ## Requirements
 
-- The model must provide valid token ids for `think_start_id`, `think_end_id`, and `line_break_id` (via `ModelConfig`).
-- If any of these ids are invalid, the processor is disabled and `thinking_budget` will not take effect.
+- The model must provide valid token ids for `think_start_id` and `think_end_id` (via `ModelConfig`).
+- If either of these ids is invalid, the processor is disabled and `thinking_budget` will not take effect.
 
 ## Request Parameters
 
-- `thinking_budget` (int, required to enable): maximum number of tokens after `<think>` before forced termination.
-- `think_stop_sentence` (string, optional): a stop sentence that will be tokenized on the CPU side and enforced near the budget boundary.
+- `thinking_budget` (int, required to enable): maximum number of decode-time tokens after `<think>` before forced
+  termination.
+- `think_stop_sentence` (string, optional): a literal custom sentence that will be tokenized on the request side
+  and enforced near the budget boundary.
 
 ## Operator-Level vs LogitsProcessor
 
@@ -41,16 +46,25 @@ FastDeploy has two ways to limit thinking length:
 In short:
 
 - If you only need a hard cap on thinking length, prefer `reasoning_max_tokens`.
-- If you need custom behavior (for example, injecting custom sentence tokens), use `ThinkingBudgetLogitsProcessor`.
+- If you need custom behavior (for example, inserting a custom sentence before `</think>`), use
+  `ThinkingBudgetLogitsProcessor`.
 
 ## Practical guidance
 
 `reasoning_max_tokens` and `thinking_budget` are not mutually exclusive in current implementation.
 If both are configured for the same request, both constraints can take effect, and whichever triggers first will end the thinking phase.
 
-- To use **operator-level-only** behavior: this is request-level config only. Set `enable_thinking=true` and `reasoning_max_tokens` in request, and do not set `thinking_budget`.
-- To use **logits-processor-only** behavior (especially with `think_stop_sentence`): this requires service-level + request-level config. Start service with `--logits-processors ThinkingBudgetLogitsProcessor`, and set `thinking_budget` (and optional `think_stop_sentence`) in `logits_processors_args`; leave `reasoning_max_tokens` unset.
-- Avoid enabling both for strict custom sentence insertion requirements, because operator-level termination may cut the custom sentence path earlier.
+- To use **operator-level-only** behavior: this is request-level config only. Set
+  `enable_thinking=true` and `reasoning_max_tokens` in request, and do not set `thinking_budget`.
+- To use **logits-processor-only** behavior (especially with `think_stop_sentence`): this requires
+  service-level + request-level config. Start service with `--logits-processors ThinkingBudgetLogitsProcessor`,
+  and set `thinking_budget` (and optional `think_stop_sentence`) in `logits_processors_args`; leave
+  `reasoning_max_tokens` unset.
+- `thinking_budget` itself does not require `enable_thinking=true`.
+- If an ERNIE chat template already appends `<think>` in the prompt, `thinking_budget` should still take effect; it
+  does not require the model to emit another `<think>` during decoding.
+- Avoid enabling both for strict custom sentence insertion requirements, because operator-level
+  termination may cut the custom sentence path earlier.
 
 ## Online Usage
 
@@ -120,4 +134,6 @@ print(outputs[0].outputs.text)
 
 ## Performance Note
 
-This processor runs `update_state` and `apply` on every decode step. If you only need a hard thinking-length cap and care most about throughput, consider the operator-level reasoning-length controls instead of per-step logits processing.
+This processor runs `update_state` and `apply` on every decode step. If you only need a hard
+thinking-length cap and care most about throughput, consider the operator-level reasoning-length
+controls instead of per-step logits processing.

docs/zh/features/thinking_budget.md

@@ -2,7 +2,9 @@
 
 ## 概述
 
-`ThinkingBudgetLogitsProcessor` 用于限制 `<think> ... </think>` 区间的生成长度。当预算达到阈值时，会强制生成换行符 token，再强制生成 `</think>`，从而结束思考段。
+`ThinkingBudgetLogitsProcessor` 用于限制 `<think> ... </think>` 区间的生成长度。当预算达到阈值时，
+会直接强制生成 `</think>` 来结束思考段；如果配置了 `think_stop_sentence`，则会先强制输出该自定义
+文案，再输出 `</think>`。
 
 ## 适用场景
 
@@ -11,19 +13,20 @@
 
 ## 工作原理
 
-1. **CPU 侧预计算（DataProcessor）**：当请求中包含 `thinking_budget`，会基于 prompt 的 token ids 计算是否已进入思考段、是否已结束，以及已有的思考长度。
+1. **请求侧预计算（DataProcessor）**：当请求中包含 `thinking_budget`，会基于 prompt 的 token ids 计算是否已进入思考段、是否已结束，以及已有的思考长度。
 2. **每步更新**：解码过程中跟踪 `last_token_id` 与 `tokens_after_start`。
-3. **预算约束**：达到预算后，依次强制换行符与思考结束 token。
+3. **预算约束**：达到预算后，默认直接强制 `</think>`；如果配置了 `think_stop_sentence`，则先逐 token
+   强制输出该文案，再输出 `</think>`。
 
 ## 前置要求
 
-- 模型需提供有效的 `think_start_id`、`think_end_id`、`line_break_id`（来自 `ModelConfig`）。
-- 若任意 id 无效，处理器会禁用，`thinking_budget` 不生效。
+- 模型需提供有效的 `think_start_id`、`think_end_id`（来自 `ModelConfig`）。
+- 若其中任意 id 无效，处理器会禁用，`thinking_budget` 不生效。
 
 ## 请求参数
 
-- `thinking_budget`（int，启用所需）：`<think>` 之后允许的最大 token 数。
-- `think_stop_sentence`（string，可选）：CPU 侧会将该字符串编码为 token ids，并在预算边界附近强制输出。
+- `thinking_budget`（int，启用所需）：`<think>` 之后允许的最大 decode 阶段 token 数。
+- `think_stop_sentence`（string，可选）：按字面串编码的自定义终止文案，并在预算边界附近强制输出。
 
 ## 算子级限制 vs LogitsProcessor
 
@@ -35,21 +38,27 @@ FastDeploy 当前有两种思考长度控制方式：
   - 适合“只限制思考长度”的简单场景。
 - **`ThinkingBudgetLogitsProcessor`**（`logits_processors_args.thinking_budget`）：
   - 由每步 Python 侧 logits 处理实现。
-  - 支持更灵活的行为，例如 `think_stop_sentence`（在结束前插入自定义话术）。
+  - 支持更灵活的行为，例如 `think_stop_sentence`。
   - 相比算子级限制，在高并发下通常有更高开销。
 
 可按以下原则选择：
 
 - 仅需限制思考长度：优先用 `reasoning_max_tokens`。
-- 需要更灵活控制（如插入自定义话术）：使用 `ThinkingBudgetLogitsProcessor`。
+- 需要更灵活控制（如在 `</think>` 前插入自定义话术）：使用 `ThinkingBudgetLogitsProcessor`。
 
 ## 建议实践
 
 当前实现中，`reasoning_max_tokens` 与 `thinking_budget` 不是互斥关系。
 同一请求如果同时配置，两套约束都可能生效，谁先触发就先结束思考段。
 
-- **只用算子级限制**：这是请求级配置。仅在请求中设置 `enable_thinking=true` + `reasoning_max_tokens`，不要传 `thinking_budget`。
-- **只用 LogitsProcessor**（尤其要用 `think_stop_sentence`）：这是“服务启动 + 请求参数”两级配置。服务启动时必须加 `--logits-processors ThinkingBudgetLogitsProcessor`，并在请求里通过 `logits_processors_args` 传 `thinking_budget`（以及可选的 `think_stop_sentence`）；同时不要设置 `reasoning_max_tokens`。
+- **只用算子级限制**：这是请求级配置。仅在请求中设置 `enable_thinking=true` + `reasoning_max_tokens`，
+  不要传 `thinking_budget`。
+- **只用 LogitsProcessor**（尤其要用 `think_stop_sentence`）：这是“服务启动 + 请求参数”两级配置。
+  服务启动时必须加 `--logits-processors ThinkingBudgetLogitsProcessor`，并在请求里通过
+  `logits_processors_args` 传 `thinking_budget`（以及可选的 `think_stop_sentence`）；同时不要设置
+  `reasoning_max_tokens`。
+- `thinking_budget` 本身不依赖 `enable_thinking=true`。
+- 如果 ERNIE 的 chat template 已经在 prompt 里拼入 `<think>`，`thinking_budget` 也应正常生效，不要求模型在 decode 阶段再次输出 `<think>`。
 - 如果业务要求“必须完整插入自定义话术”，不建议与算子级限制同时开启，否则可能被算子级提前截断。
 
 ## 在线使用

fastdeploy/input/ernie4_5_processor.py

@@ -107,6 +107,11 @@ class Ernie4_5Processor(BaseDataProcessor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request["bad_words_token_ids"] = bad_words_token_ids
 
+        logits_processors_args = self._prepare_think_stop_sentence(
+            request.get("logits_processors_args") or {}, max_model_len
+        )
+        request["logits_processors_args"] = logits_processors_args
+
         # processing prompt_token_ids
         if not request.get("prompt_token_ids"):
             if request.get("prompt"):
@@ -143,6 +148,10 @@ class Ernie4_5Processor(BaseDataProcessor):
         # truncate prompts that exceed the length limit
         if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
             request["prompt_token_ids"] = request["prompt_token_ids"][: max_model_len - 1]
+        logits_processors_args = self._update_thinking_prompt_state(
+            request["prompt_token_ids"], request.get("logits_processors_args") or {}
+        )
+        request["logits_processors_args"] = logits_processors_args
         max_tokens = max_model_len - len(request["prompt_token_ids"])
         if request.get("max_tokens") is None:
             request["max_tokens"] = max(1, max_tokens)

fastdeploy/input/ernie4_5_vl_processor/ernie4_5_vl_processor.py

@@ -210,6 +210,11 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request["bad_words_token_ids"] = bad_words_token_ids
 
+        logits_processors_args = self._prepare_think_stop_sentence(
+            request.get("logits_processors_args") or {}, max_model_len
+        )
+        request["logits_processors_args"] = logits_processors_args
+
         if request.get("prompt_token_ids"):
             messages = request.get("messages")
             if messages:
@@ -257,6 +262,10 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
         # 截断超过长度限制的prompt
         if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
             request["prompt_token_ids"] = request["prompt_token_ids"][: max_model_len - 1]
+        logits_processors_args = self._update_thinking_prompt_state(
+            request["prompt_token_ids"], request.get("logits_processors_args") or {}
+        )
+        request["logits_processors_args"] = logits_processors_args
 
         max_tokens = max_model_len - len(request["prompt_token_ids"])
         if request.get("max_tokens") is None:

fastdeploy/input/text_processor.py

@@ -92,6 +92,9 @@ class BaseDataProcessor(ABC):
         """
         Encode text into token ids with a small LRU cache.
         """
+        if not hasattr(self, "_tokenize_cache"):
+            self._tokenize_cache = OrderedDict()
+            self._tokenize_cache_capacity = getattr(self, "_tokenize_cache_capacity", 128)
         key = (text, bool(add_special_tokens))
         cached = self._tokenize_cache.get(key)
         if cached is not None:
@@ -107,6 +110,25 @@ class BaseDataProcessor(ABC):
             self._tokenize_cache.popitem(last=False)
         return token_ids
 
+    def _encode_literal_text_with_cache(self, text):
+        if not hasattr(self, "_tokenize_cache"):
+            self._tokenize_cache = OrderedDict()
+            self._tokenize_cache_capacity = getattr(self, "_tokenize_cache_capacity", 128)
+        key = ("literal_text", text)
+        cached = self._tokenize_cache.get(key)
+        if cached is not None:
+            self._tokenize_cache.move_to_end(key)
+            return cached
+        token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
+        if hasattr(token_ids, "tolist"):
+            token_ids = token_ids.tolist()
+        elif not isinstance(token_ids, list):
+            token_ids = list(token_ids)
+        self._tokenize_cache[key] = token_ids
+        if len(self._tokenize_cache) > self._tokenize_cache_capacity:
+            self._tokenize_cache.popitem(last=False)
+        return token_ids
+
     def messages2ids(self, messages):
         """
         Convert multi-turn messages into ID sequences.
@@ -119,6 +141,77 @@ class BaseDataProcessor(ABC):
         """
         raise NotImplementedError
 
+    def _get_think_token_ids(self):
+        think_token_ids = getattr(self, "_think_token_ids", None)
+        if think_token_ids is not None:
+            return think_token_ids
+        tokenizer = getattr(self, "tokenizer", None)
+        vocab = tokenizer.get_vocab() if tokenizer is not None else {}
+        think_start_id = vocab.get("<think>", -1)
+        think_end_id = vocab.get("</think>", -1)
+        self._think_token_ids = (think_start_id, think_end_id)
+        return self._think_token_ids
+
+    def _prepare_think_stop_sentence(self, logits_processors_args, max_model_len=None):
+        if not isinstance(logits_processors_args, dict):
+            return logits_processors_args
+        think_stop_sentence = logits_processors_args.get("think_stop_sentence")
+        if isinstance(think_stop_sentence, str) and think_stop_sentence:
+            sentence_token_ids = self._encode_literal_text_with_cache(think_stop_sentence)
+            logits_processors_args["think_stop_sentence_token_ids"] = sentence_token_ids
+            logits_processors_args.pop("think_stop_sentence", None)
+        return logits_processors_args
+
+    def _update_thinking_prompt_state(self, prompt_token_ids, logits_processors_args):
+        if not isinstance(logits_processors_args, dict):
+            return logits_processors_args
+        thinking_budget = logits_processors_args.get("thinking_budget")
+        if thinking_budget is None or not isinstance(thinking_budget, int) or thinking_budget < 0:
+            return logits_processors_args
+        if logits_processors_args.get("think_prompt_checked"):
+            return logits_processors_args
+        if prompt_token_ids is None:
+            return logits_processors_args
+        token_len = getattr(prompt_token_ids, "size", None) or len(prompt_token_ids)
+        if token_len == 0:
+            return logits_processors_args
+        think_start_id, think_end_id = self._get_think_token_ids()
+        if think_start_id < 0 or think_end_id < 0:
+            return logits_processors_args
+
+        if hasattr(prompt_token_ids, "tolist"):
+            token_list = prompt_token_ids.tolist()
+        else:
+            token_list = list(prompt_token_ids)
+
+        started = False
+        ended = False
+        tokens_after_start = 0
+        last_token_id = None
+        in_thinking = False
+        for token_id in token_list:
+            if token_id == think_start_id:
+                started = True
+                ended = False
+                in_thinking = True
+            elif token_id == think_end_id and in_thinking:
+                ended = True
+                in_thinking = False
+        if started and token_list:
+            # Align with operator-level reasoning_max_tokens: prompt-side tokens
+            # inside <think> do not consume thinking budget.
+            last_token_id = int(token_list[-1])
+
+        logits_processors_args["think_prompt_checked"] = True
+        logits_processors_args["think_prompt_started"] = started
+        logits_processors_args["think_prompt_ended"] = ended
+        logits_processors_args["think_prompt_tokens_after_start"] = tokens_after_start
+        if last_token_id is not None:
+            logits_processors_args["think_prompt_last_token_id"] = last_token_id
+        else:
+            logits_processors_args.pop("think_prompt_last_token_id", None)
+        return logits_processors_args
+
     def ids2tokens(self, token_id, task_id=None):
         """
         token ids to strings
@@ -207,65 +300,6 @@ class DataProcessor(BaseDataProcessor):
             self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
         self.tokenizer.pad_token_id = self.pad_token_id
 
-        self._think_token_ids = None
-
-    def _get_think_token_ids(self):
-        if self._think_token_ids is not None:
-            return self._think_token_ids
-        vocab = self.tokenizer.get_vocab()
-        think_start_id = vocab.get("<think>", -1)
-        think_end_id = vocab.get("</think>", -1)
-        self._think_token_ids = (think_start_id, think_end_id)
-        return self._think_token_ids
-
-    def _update_thinking_prompt_state(self, prompt_token_ids, logits_processors_args):
-        if not isinstance(logits_processors_args, dict):
-            return logits_processors_args
-        thinking_budget = logits_processors_args.get("thinking_budget")
-        if thinking_budget is None or not isinstance(thinking_budget, int) or thinking_budget < 0:
-            return logits_processors_args
-        if logits_processors_args.get("think_prompt_checked"):
-            return logits_processors_args
-        if prompt_token_ids is None:
-            return logits_processors_args
-        token_len = getattr(prompt_token_ids, "size", None) or len(prompt_token_ids)
-        if token_len == 0:
-            return logits_processors_args
-        think_start_id, think_end_id = self._get_think_token_ids()
-        if think_start_id < 0 or think_end_id < 0:
-            return logits_processors_args
-
-        if hasattr(prompt_token_ids, "tolist"):
-            token_list = prompt_token_ids.tolist()
-        else:
-            token_list = list(prompt_token_ids)
-
-        started = think_start_id in token_list
-        ended = False
-        tokens_after_start = 0
-        last_token_id = None
-        if started:
-            start_pos = token_list.index(think_start_id)
-            tokens_after = token_list[start_pos + 1 :]
-            if think_end_id in tokens_after:
-                end_pos = tokens_after.index(think_end_id)
-                tokens_after_start = end_pos + 1
-                ended = True
-            else:
-                tokens_after_start = len(tokens_after)
-            if token_list:
-                last_token_id = int(token_list[-1])
-
-        logits_processors_args["think_prompt_checked"] = True
-        logits_processors_args["think_prompt_started"] = started
-        logits_processors_args["think_prompt_ended"] = ended
-        logits_processors_args["think_prompt_tokens_after_start"] = tokens_after_start
-        if last_token_id is not None:
-            logits_processors_args["think_prompt_last_token_id"] = last_token_id
-        else:
-            logits_processors_args.pop("think_prompt_last_token_id", None)
-        return logits_processors_args
-
     def process_request_dict(self, request, max_model_len=None, **kwargs):
         """
         Preprocess the request
@@ -292,14 +326,10 @@ class DataProcessor(BaseDataProcessor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request["bad_words_token_ids"] = bad_words_token_ids
 
-        logits_processors_args = request.get("logits_processors_args") or {}
-        think_stop_sentence = logits_processors_args.get("think_stop_sentence")
-        if isinstance(think_stop_sentence, str) and think_stop_sentence:
-            newline_token_ids = self.encode_with_cache("\n", max_model_len, add_special_tokens=False)
-            sentence_token_ids = self.encode_with_cache(think_stop_sentence, max_model_len, add_special_tokens=False)
-            logits_processors_args["think_stop_sentence_token_ids"] = newline_token_ids + sentence_token_ids
-            logits_processors_args.pop("think_stop_sentence", None)
-            request["logits_processors_args"] = logits_processors_args
+        logits_processors_args = self._prepare_think_stop_sentence(
+            request.get("logits_processors_args") or {}, max_model_len
+        )
+        request["logits_processors_args"] = logits_processors_args
 
         # processing prompt_token_ids
         if not request.get("prompt_token_ids"):

fastdeploy/input/v1/ernie4_5_processor.py

@@ -107,6 +107,11 @@ class Ernie4_5Processor(BaseDataProcessor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request["bad_words_token_ids"] = bad_words_token_ids
 
+        logits_processors_args = self._prepare_think_stop_sentence(
+            request.get("logits_processors_args") or {}, max_model_len
+        )
+        request["logits_processors_args"] = logits_processors_args
+
         # processing prompt_token_ids
         if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
             if request.prompt is not None:
@@ -123,7 +128,8 @@ class Ernie4_5Processor(BaseDataProcessor):
                     token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
                     request.prompt_token_ids = token_ids
                     data_processor_logger.debug(
-                        f"request_ids: {request.request_id}, prompt: {prompt}, tokens: {tokens}, token_ids: {token_ids}"
+                        f"request_ids: {request.request_id}, prompt: {prompt}, "
+                        f"tokens: {tokens}, token_ids: {token_ids}"
                     )
             elif request.messages is not None:
                 task = request.to_dict()
@@ -145,6 +151,10 @@ class Ernie4_5Processor(BaseDataProcessor):
         # truncate prompts that exceed the length limit
         if max_model_len is not None and len(request.prompt_token_ids) > max_model_len:
             request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1]
+        logits_processors_args = self._update_thinking_prompt_state(
+            request.prompt_token_ids, request.get("logits_processors_args") or {}
+        )
+        request["logits_processors_args"] = logits_processors_args
         max_tokens = max_model_len - len(request.prompt_token_ids)
         if request.get("max_tokens") is None:
             request.set("max_tokens", max(1, max_tokens))
@@ -201,6 +211,11 @@ class Ernie4_5Processor(BaseDataProcessor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request.sampling_params.bad_words_token_ids = bad_words_token_ids
 
+        logits_processors_args = self._prepare_think_stop_sentence(
+            getattr(request.sampling_params, "logits_processors_args", None) or {}, max_model_len
+        )
+        request.sampling_params.logits_processors_args = logits_processors_args
+
         # processing prompt_token_ids
         if not request.prompt_token_ids:
             if request.prompt:
@@ -241,6 +256,10 @@ class Ernie4_5Processor(BaseDataProcessor):
         # truncate prompts that exceed the length limit
         if max_model_len is not None and len(request.prompt_token_ids) > max_model_len:
             request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1]
+        logits_processors_args = self._update_thinking_prompt_state(
+            request.prompt_token_ids, getattr(request.sampling_params, "logits_processors_args", None) or {}
+        )
+        request.sampling_params.logits_processors_args = logits_processors_args
         max_tokens = max_model_len - len(request.prompt_token_ids)
         if getattr(request.sampling_params, "max_tokens", None) is None:
             request.sampling_params.max_tokens = max(1, max_tokens)

fastdeploy/input/v1/ernie4_5_vl_processor/ernie4_5_vl_processor.py

@@ -216,6 +216,11 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request.sampling_params.bad_words_token_ids = bad_words_token_ids
 
+        logits_processors_args = self._prepare_think_stop_sentence(
+            getattr(request.sampling_params, "logits_processors_args", None) or {}, max_model_len
+        )
+        request.sampling_params.logits_processors_args = logits_processors_args
+
         if request.prompt_token_ids:
             messages = request.messages
             if messages:
@@ -267,6 +272,10 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
         # 截断超过长度限制的prompt
         if max_model_len is not None and len(request.prompt_token_ids) > max_model_len:
             request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1]
+        logits_processors_args = self._update_thinking_prompt_state(
+            request.prompt_token_ids, getattr(request.sampling_params, "logits_processors_args", None) or {}
+        )
+        request.sampling_params.logits_processors_args = logits_processors_args
 
         max_tokens = max_model_len - len(request.prompt_token_ids)
         if getattr(request.sampling_params, "max_tokens", None) is None:

fastdeploy/input/v1/text_processor.py

@@ -115,6 +115,9 @@ class BaseDataProcessor(ABC):
         """
         Encode text into token ids with a small LRU cache.
         """
+        if not hasattr(self, "_tokenize_cache"):
+            self._tokenize_cache = OrderedDict()
+            self._tokenize_cache_capacity = getattr(self, "_tokenize_cache_capacity", 128)
         key = (text, bool(add_special_tokens))
         cached = self._tokenize_cache.get(key)
         if cached is not None:
@@ -130,6 +133,25 @@ class BaseDataProcessor(ABC):
             self._tokenize_cache.popitem(last=False)
         return token_ids
 
+    def _encode_literal_text_with_cache(self, text):
+        if not hasattr(self, "_tokenize_cache"):
+            self._tokenize_cache = OrderedDict()
+            self._tokenize_cache_capacity = getattr(self, "_tokenize_cache_capacity", 128)
+        key = ("literal_text", text)
+        cached = self._tokenize_cache.get(key)
+        if cached is not None:
+            self._tokenize_cache.move_to_end(key)
+            return cached
+        token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
+        if hasattr(token_ids, "tolist"):
+            token_ids = token_ids.tolist()
+        elif not isinstance(token_ids, list):
+            token_ids = list(token_ids)
+        self._tokenize_cache[key] = token_ids
+        if len(self._tokenize_cache) > self._tokenize_cache_capacity:
+            self._tokenize_cache.popitem(last=False)
+        return token_ids
+
     def messages2ids(self, messages):
         """
         Convert multi-turn messages into ID sequences.
@@ -142,6 +164,77 @@ class BaseDataProcessor(ABC):
         """
         raise NotImplementedError
 
+    def _get_think_token_ids(self):
+        think_token_ids = getattr(self, "_think_token_ids", None)
+        if think_token_ids is not None:
+            return think_token_ids
+        tokenizer = getattr(self, "tokenizer", None)
+        vocab = tokenizer.get_vocab() if tokenizer is not None else {}
+        think_start_id = vocab.get("<think>", -1)
+        think_end_id = vocab.get("</think>", -1)
+        self._think_token_ids = (think_start_id, think_end_id)
+        return self._think_token_ids
+
+    def _prepare_think_stop_sentence(self, logits_processors_args, max_model_len=None):
+        if not isinstance(logits_processors_args, dict):
+            return logits_processors_args
+        think_stop_sentence = logits_processors_args.get("think_stop_sentence")
+        if isinstance(think_stop_sentence, str) and think_stop_sentence:
+            sentence_token_ids = self._encode_literal_text_with_cache(think_stop_sentence)
+            logits_processors_args["think_stop_sentence_token_ids"] = sentence_token_ids
+            logits_processors_args.pop("think_stop_sentence", None)
+        return logits_processors_args
+
+    def _update_thinking_prompt_state(self, prompt_token_ids, logits_processors_args):
+        if not isinstance(logits_processors_args, dict):
+            return logits_processors_args
+        thinking_budget = logits_processors_args.get("thinking_budget")
+        if thinking_budget is None or not isinstance(thinking_budget, int) or thinking_budget < 0:
+            return logits_processors_args
+        if logits_processors_args.get("think_prompt_checked"):
+            return logits_processors_args
+        if prompt_token_ids is None:
+            return logits_processors_args
+        token_len = getattr(prompt_token_ids, "size", None) or len(prompt_token_ids)
+        if token_len == 0:
+            return logits_processors_args
+        think_start_id, think_end_id = self._get_think_token_ids()
+        if think_start_id < 0 or think_end_id < 0:
+            return logits_processors_args
+
+        if hasattr(prompt_token_ids, "tolist"):
+            token_list = prompt_token_ids.tolist()
+        else:
+            token_list = list(prompt_token_ids)
+
+        started = False
+        ended = False
+        tokens_after_start = 0
+        last_token_id = None
+        in_thinking = False
+        for token_id in token_list:
+            if token_id == think_start_id:
+                started = True
+                ended = False
+                in_thinking = True
+            elif token_id == think_end_id and in_thinking:
+                ended = True
+                in_thinking = False
+        if started and token_list:
+            # Align with operator-level reasoning_max_tokens: prompt-side tokens
+            # inside <think> do not consume thinking budget.
+            last_token_id = int(token_list[-1])
+
+        logits_processors_args["think_prompt_checked"] = True
+        logits_processors_args["think_prompt_started"] = started
+        logits_processors_args["think_prompt_ended"] = ended
+        logits_processors_args["think_prompt_tokens_after_start"] = tokens_after_start
+        if last_token_id is not None:
+            logits_processors_args["think_prompt_last_token_id"] = last_token_id
+        else:
+            logits_processors_args.pop("think_prompt_last_token_id", None)
+        return logits_processors_args
+
     def ids2tokens(self, token_id, task_id=None):
         """
         token ids to strings
@@ -221,65 +314,6 @@ class DataProcessor(BaseDataProcessor):
             self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
         self.tokenizer.pad_token_id = self.pad_token_id
 
-        self._think_token_ids = None
-
-    def _get_think_token_ids(self):
-        if self._think_token_ids is not None:
-            return self._think_token_ids
-        vocab = self.tokenizer.get_vocab()
-        think_start_id = vocab.get("<think>", -1)
-        think_end_id = vocab.get("</think>", -1)
-        self._think_token_ids = (think_start_id, think_end_id)
-        return self._think_token_ids
-
-    def _update_thinking_prompt_state(self, prompt_token_ids, logits_processors_args):
-        if not isinstance(logits_processors_args, dict):
-            return logits_processors_args
-        thinking_budget = logits_processors_args.get("thinking_budget")
-        if thinking_budget is None or not isinstance(thinking_budget, int) or thinking_budget < 0:
-            return logits_processors_args
-        if logits_processors_args.get("think_prompt_checked"):
-            return logits_processors_args
-        if prompt_token_ids is None:
-            return logits_processors_args
-        token_len = getattr(prompt_token_ids, "size", None) or len(prompt_token_ids)
-        if token_len == 0:
-            return logits_processors_args
-        think_start_id, think_end_id = self._get_think_token_ids()
-        if think_start_id < 0 or think_end_id < 0:
-            return logits_processors_args
-
-        if hasattr(prompt_token_ids, "tolist"):
-            token_list = prompt_token_ids.tolist()
-        else:
-            token_list = list(prompt_token_ids)
-
-        started = think_start_id in token_list
-        ended = False
-        tokens_after_start = 0
-        last_token_id = None
-        if started:
-            start_pos = token_list.index(think_start_id)
-            tokens_after = token_list[start_pos + 1 :]
-            if think_end_id in tokens_after:
-                end_pos = tokens_after.index(think_end_id)
-                tokens_after_start = end_pos + 1
-                ended = True
-            else:
-                tokens_after_start = len(tokens_after)
-            if token_list:
-                last_token_id = int(token_list[-1])
-
-        logits_processors_args["think_prompt_checked"] = True
-        logits_processors_args["think_prompt_started"] = started
-        logits_processors_args["think_prompt_ended"] = ended
-        logits_processors_args["think_prompt_tokens_after_start"] = tokens_after_start
-        if last_token_id is not None:
-            logits_processors_args["think_prompt_last_token_id"] = last_token_id
-        else:
-            logits_processors_args.pop("think_prompt_last_token_id", None)
-        return logits_processors_args
-
     def process_request(self, request, max_model_len=None, **kwargs):
         """
         Preprocess the request
@@ -306,14 +340,10 @@ class DataProcessor(BaseDataProcessor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request["bad_words_token_ids"] = bad_words_token_ids
 
-        logits_processors_args = request.get("logits_processors_args") or {}
-        think_stop_sentence = logits_processors_args.get("think_stop_sentence")
-        if isinstance(think_stop_sentence, str) and think_stop_sentence:
-            newline_token_ids = self.encode_with_cache("\n", max_model_len, add_special_tokens=False)
-            sentence_token_ids = self.encode_with_cache(think_stop_sentence, max_model_len, add_special_tokens=False)
-            logits_processors_args["think_stop_sentence_token_ids"] = newline_token_ids + sentence_token_ids
-            logits_processors_args.pop("think_stop_sentence", None)
-            request["logits_processors_args"] = logits_processors_args
+        logits_processors_args = self._prepare_think_stop_sentence(
+            request.get("logits_processors_args") or {}, max_model_len
+        )
+        request["logits_processors_args"] = logits_processors_args
 
         # processing prompt_token_ids
         if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
@@ -411,14 +441,10 @@ class DataProcessor(BaseDataProcessor):
             bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
             request.sampling_params.bad_words_token_ids = bad_words_token_ids
 
-        logits_processors_args = getattr(request.sampling_params, "logits_processors_args", None) or {}
-        think_stop_sentence = logits_processors_args.get("think_stop_sentence")
-        if isinstance(think_stop_sentence, str) and think_stop_sentence:
-            newline_token_ids = self.encode_with_cache("\n", max_model_len, add_special_tokens=False)
-            sentence_token_ids = self.encode_with_cache(think_stop_sentence, max_model_len, add_special_tokens=False)
-            logits_processors_args["think_stop_sentence_token_ids"] = newline_token_ids + sentence_token_ids
-            logits_processors_args.pop("think_stop_sentence", None)
-            request.sampling_params.logits_processors_args = logits_processors_args
+        logits_processors_args = self._prepare_think_stop_sentence(
+            getattr(request.sampling_params, "logits_processors_args", None) or {}, max_model_len
+        )
+        request.sampling_params.logits_processors_args = logits_processors_args
 
         # processing prompt_token_ids
         if not request.prompt_token_ids:

fastdeploy/model_executor/logits_processor/thinking_budget.py

@@ -42,14 +42,15 @@ class _ThinkingState:
 class ThinkingBudgetLogitsProcessor(LogitsProcessor):
     """Limit the number of tokens generated in the thinking phase.
 
-    The processor tracks per-request thinking state and forces a newline token
-    when the budget is reached, followed by the thinking end token on the next step.
+    The processor tracks per-request thinking state and forces the thinking end
+    token when the budget is reached. If a stop sentence is configured, the
+    processor emits the stop sentence first and then the thinking end token.
     Request-specific configuration is provided via logits_processors_args:
 
         {"thinking_budget": <int>}
 
-    Requires model_config to provide think_start_id, think_end_id, and line_break_id.
-    If any of these are missing or invalid (-1), the processor will be disabled.
+    Requires model_config to provide think_start_id and think_end_id. If any of
+    these are missing or invalid (-1), the processor will be disabled.
     """
 
     def __init__(self, fd_config: FDConfig) -> None:
@@ -61,20 +62,33 @@ class ThinkingBudgetLogitsProcessor(LogitsProcessor):
         self.think_start_token_id = think_start_id if isinstance(think_start_id, int) and think_start_id >= 0 else -1
         self.think_end_token_id = think_end_id if isinstance(think_end_id, int) and think_end_id >= 0 else -1
         self.line_break_token_id = line_break_id if isinstance(line_break_id, int) and line_break_id >= 0 else -1
-        self._enabled = (
-            self.think_start_token_id >= 0 and self.think_end_token_id >= 0 and self.line_break_token_id >= 0
-        )
+        self._enabled = self.think_start_token_id >= 0 and self.think_end_token_id >= 0
         if not self._enabled:
             logger.warning(
                 "ThinkingBudgetLogitsProcessor disabled: missing token ids "
-                f"(think_start={think_start_id}, think_end={think_end_id}, line_break={line_break_id}). "
-                "Ensure model vocab contains <think>, </think> tokens and line_break_id is configured."
+                f"(think_start={think_start_id}, think_end={think_end_id}). "
+                "Ensure model vocab contains <think> and </think> tokens."
             )
         self._states: Dict[str, _ThinkingState] = {}
         self._active_req_ids: list[str] = []
         self._active_budgets: list[int] = []
         self._active_slots: list[int] = []
 
+    def _scan_prompt_state(self, prompt_slice: list[int]) -> tuple[bool, bool, int, Optional[int]]:
+        started = False
+        ended = False
+        in_thinking = False
+        for token_id in prompt_slice:
+            if token_id == self.think_start_token_id:
+                started = True
+                ended = False
+                in_thinking = True
+            elif token_id == self.think_end_token_id and in_thinking:
+                ended = True
+                in_thinking = False
+        last_token_id = int(prompt_slice[-1]) if started and prompt_slice else None
+        return started, ended, 0, last_token_id
+
     def update_state(self, share_inputs: dict) -> None:
         if not self._enabled:
             return
@@ -82,6 +96,7 @@ class ThinkingBudgetLogitsProcessor(LogitsProcessor):
         req_ids = share_inputs["req_ids"]
         logits_processors_args = share_inputs["logits_processors_args"]
         prompt_ids = share_inputs.get("prompt_ids")
+        token_ids_all = share_inputs.get("token_ids_all")
         prompt_lens = share_inputs.get("prompt_lens")
         pre_ids = share_inputs.get("pre_ids")
         next_tokens = share_inputs.get("next_tokens")
@@ -153,6 +168,8 @@ class ThinkingBudgetLogitsProcessor(LogitsProcessor):
             for idx, slot_id in enumerate(candidate_slots):
                 next_token_by_slot[slot_id] = int(next_sel[idx])
 
+        prompt_source = prompt_ids if prompt_ids is not None else token_ids_all
+
         for idx, slot_id in enumerate(candidate_slots):
             req_id = candidate_req_ids[idx]
             logit_proc_args = candidate_args[idx]
@@ -190,25 +207,28 @@ class ThinkingBudgetLogitsProcessor(LogitsProcessor):
             state.current_step_idx = current_step_idx
 
             if not state.started and not state.prompt_checked:
-                if prompt_ids is not None and prompt_lens is not None:
+                if prompt_source is not None and prompt_lens is not None:
                     if prompt_lens_np is not None:
                         prompt_len = int(prompt_lens_np[slot_id])
                     else:
                         prompt_len = int(prompt_lens[slot_id])
-                    prompt_slice = prompt_ids[slot_id, :prompt_len]
-                    prompt_slice = prompt_slice.numpy().tolist()
-                    if self.think_start_token_id in prompt_slice:
+                    prompt_slice = prompt_source[slot_id, :prompt_len]
+                    if hasattr(prompt_slice, "numpy"):
+                        prompt_slice = prompt_slice.numpy().tolist()
+                    elif hasattr(prompt_slice, "tolist"):
+                        prompt_slice = prompt_slice.tolist()
+                    else:
+                        prompt_slice = list(prompt_slice)
+                    if prompt_ids is None:
+                        prompt_slice = [int(token_id) for token_id in prompt_slice if int(token_id) >= 0]
+                    prompt_started, prompt_ended, prompt_tokens_after_start, prompt_last_token_id = (
+                        self._scan_prompt_state(prompt_slice)
+                    )
+                    if prompt_started:
                         state.started = True
-                        start_pos = prompt_slice.index(self.think_start_token_id)
-                        tokens_after = prompt_slice[start_pos + 1 :]
-                        if self.think_end_token_id in tokens_after:
-                            end_pos = tokens_after.index(self.think_end_token_id)
-                            state.tokens_after_start = end_pos + 1
-                            state.ended = True
-                        else:
-                            state.tokens_after_start = len(tokens_after)
-                        if prompt_slice:
-                            state.last_token_id = int(prompt_slice[-1])
+                        state.ended = prompt_ended
+                        state.tokens_after_start = prompt_tokens_after_start
+                        state.last_token_id = prompt_last_token_id
                         if current_step_idx is not None and state.last_step_idx is None:
                             state.last_step_idx = current_step_idx
                     state.prompt_checked = True
@@ -304,13 +324,6 @@ class ThinkingBudgetLogitsProcessor(LogitsProcessor):
             if state.tokens_after_start < budget:
                 continue
 
-            if state.last_token_id != self.line_break_token_id:
-                logits[slot_id, :] = -float("inf")
-                logits[slot_id, self.line_break_token_id] = 0.0
-                state.last_token_id = self.line_break_token_id
-                state.last_step_idx = state.current_step_idx
-                continue
-
             logits[slot_id, :] = -float("inf")
             logits[slot_id, self.think_end_token_id] = 0.0
             state.last_token_id = self.think_end_token_id

tests/model_executor/test_thinking_budget.py

@@ -20,7 +20,19 @@ from fastdeploy.engine import common_engine as common_engine_module
 from fastdeploy.engine import engine as engine_module
 from fastdeploy.engine.args_utils import EngineArgs  # Import EngineArgs
 from fastdeploy.engine.sampling_params import SamplingParams
+from fastdeploy.input.ernie4_5_processor import (
+    Ernie4_5Processor as ErnieTextDataProcessor,
+)
+from fastdeploy.input.ernie4_5_vl_processor import (
+    Ernie4_5_VLProcessor as ErnieVLDataProcessor,
+)
 from fastdeploy.input.text_processor import DataProcessor as TextDataProcessor
+from fastdeploy.input.v1.ernie4_5_processor import (
+    Ernie4_5Processor as V1ErnieTextDataProcessor,
+)
+from fastdeploy.input.v1.ernie4_5_vl_processor.ernie4_5_vl_processor import (
+    Ernie4_5_VLProcessor as V1ErnieVLDataProcessor,
+)
 from fastdeploy.input.v1.text_processor import DataProcessor as V1TextDataProcessor
 from fastdeploy.model_executor.logits_processor import ThinkingBudgetLogitsProcessor
 from fastdeploy.scheduler import SchedulerConfig
@@ -52,6 +64,7 @@ class MockModelRunner:
             "req_ids": [f"req_{i}" for i in range(max_num_seqs)],
             "logits_processors_args": [{} for _ in range(max_num_seqs)],
             "prompt_ids": paddle.to_tensor(np.zeros((max_num_seqs, 10), dtype=np.int64)),  # Max prompt len 10
+            "token_ids_all": paddle.to_tensor(np.full((max_num_seqs, self.max_model_len), -1, dtype=np.int64)),
             "prompt_lens": paddle.to_tensor(np.zeros((max_num_seqs, 1), dtype=np.int64)),
             "pre_ids": paddle.to_tensor(np.full((max_num_seqs, self.max_model_len), -1, dtype=np.int64)),
             "step_idx": paddle.to_tensor(np.zeros((max_num_seqs, 1), dtype=np.int64)),
@@ -73,6 +86,12 @@ class MockModelRunner:
         self.share_inputs["prompt_ids"][slot_id, : len(req.prompt_ids)] = paddle.to_tensor(
             req.prompt_ids, dtype=paddle.int64
         )
+        self.share_inputs["token_ids_all"][slot_id, :] = paddle.to_tensor(
+            np.full((self.max_model_len,), -1, dtype=np.int64), dtype=paddle.int64
+        )
+        self.share_inputs["token_ids_all"][slot_id, : len(req.prompt_ids)] = paddle.to_tensor(
+            req.prompt_ids, dtype=paddle.int64
+        )
         self.share_inputs["prompt_lens"][slot_id] = paddle.to_tensor(len(req.prompt_ids), dtype=paddle.int64)
         if req.sampling_params.logits_processors_args:
             self.share_inputs["logits_processors_args"][slot_id] = req.sampling_params.logits_processors_args
@@ -152,7 +171,7 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         return logits
 
     def test_thinking_budget_not_reached(self):
-        # Scenario: Thinking budget is 5, but only 3 tokens are generated in thinking phase
+        # Scenario: Thinking budget is 5, and prompt-side tokens after <think> do not consume budget.
         req_id = "test_req_1"
         prompt_ids = [1, 2, THINKING_START_TOKEN_ID, 3, 4, 5]
         sampling_params = SamplingParams(logits_processors_args={"thinking_budget": 5})
@@ -167,9 +186,9 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         processor.update_state(mock_runner.share_inputs)
         self.assertTrue(processor._states[req_id].started)
         self.assertFalse(processor._states[req_id].ended)
-        self.assertEqual(processor._states[req_id].tokens_after_start, 3)  # (3, 4, 5) after THINKING_START
+        self.assertEqual(processor._states[req_id].tokens_after_start, 0)
 
-        # Step 2: Simulate one generation step (budget 5, generated 3 -> 4)
+        # Step 2: Simulate one generation step (budget 5, generated 0 -> 1)
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)  # Update state before apply
         processed_logits = processor.apply(logits)
@@ -181,9 +200,9 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         mock_runner.update_request_state(0, mock_req, pre_id=0)  # Update last token
 
         processor.update_state(mock_runner.share_inputs)  # Update state after generating token
-        self.assertEqual(processor._states[req_id].tokens_after_start, 4)  # (3, 4, 5, 0)
+        self.assertEqual(processor._states[req_id].tokens_after_start, 1)
 
-        # Step 3: Simulate another generation step (budget 5, generated 4 -> 5)
+        # Step 3: Simulate another generation step (budget 5, generated 1 -> 2)
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)  # Update state before apply
         processed_logits = processor.apply(logits)
@@ -191,14 +210,14 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         mock_runner.update_request_state(0, mock_req, pre_id=0)  # Update last token
 
         processor.update_state(mock_runner.share_inputs)  # Update state after generating token
-        self.assertEqual(processor._states[req_id].tokens_after_start, 5)  # (3, 4, 5, 0, 0)
+        self.assertEqual(processor._states[req_id].tokens_after_start, 2)
 
         # LogitsProcessor should still not restrict as NEW_LINE_TOKEN is not yet last token
 
-    def test_thinking_budget_reached_forces_newline(self):
-        # Scenario: Budget is 3, after 3 tokens, it should force newline, then thinking_end
+    def test_thinking_budget_reached_forces_think_end(self):
+        # Scenario: Budget is 3 and only decode-time tokens count toward the budget.
         req_id = "test_req_2"
-        prompt_ids = [1, 2, THINKING_START_TOKEN_ID, 3]  # Initial 1 token after start
+        prompt_ids = [1, 2, THINKING_START_TOKEN_ID, 3]
         sampling_params = SamplingParams(logits_processors_args={"thinking_budget": 3})
         mock_req = MockRequest(req_id, prompt_ids, sampling_params)
 
@@ -206,16 +225,26 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         mock_runner.update_request_state(0, mock_req, pre_id=prompt_ids[-1])
 
         processor = ThinkingBudgetLogitsProcessor(self.fd_config)
-        line_break_id = processor.line_break_token_id
         think_end_id = processor.think_end_token_id
 
-        # Step 1: Initial state update (1 token after start)
+        # Step 1: Initial state update (prompt-side tokens do not count)
         processor.update_state(mock_runner.share_inputs)
-        self.assertEqual(processor._states[req_id].tokens_after_start, 1)
+        self.assertEqual(processor._states[req_id].tokens_after_start, 0)
         self.assertFalse(processor._states[req_id].ended)
         self.assertEqual(processor._states[req_id].last_token_id, 3)
 
-        # Step 2: Generate 2nd token (budget 3, generated 1 -> 2)
+        # Step 2: Generate 1st decode token (budget 3, generated 0 -> 1)
+        logits = self._get_initial_logits(1)
+        processor.update_state(mock_runner.share_inputs)
+        processed_logits = processor.apply(logits)
+        next_token = mock_runner.generate_next_token(processed_logits)[0]
+        self.assertEqual(next_token, 0)  # Normal generation
+        mock_runner.update_request_state(0, mock_req, pre_id=next_token)
+        processor.update_state(mock_runner.share_inputs)
+        self.assertEqual(processor._states[req_id].tokens_after_start, 1)
+        self.assertEqual(processor._states[req_id].last_token_id, 0)
+
+        # Step 3: Generate 2nd decode token (budget 3, generated 1 -> 2)
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)
         processed_logits = processor.apply(logits)
@@ -226,42 +255,17 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         self.assertEqual(processor._states[req_id].tokens_after_start, 2)
         self.assertEqual(processor._states[req_id].last_token_id, 0)
 
-        # Step 3: Generate 3rd token (budget 3, generated 2 -> 3). Next step should force NEW_LINE.
+        # Step 4: Generate 3rd decode token (budget 3, generated 2 -> 3).
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)
         processed_logits = processor.apply(logits)
         next_token = mock_runner.generate_next_token(processed_logits)[0]
-        self.assertEqual(next_token, 0)  # Normal generation
+        self.assertEqual(next_token, 0)
         mock_runner.update_request_state(0, mock_req, pre_id=next_token)
         processor.update_state(mock_runner.share_inputs)
-        self.assertEqual(processor._states[req_id].tokens_after_start, 3)  # Budget is now met
-        self.assertEqual(processor._states[req_id].last_token_id, 0)
+        self.assertEqual(processor._states[req_id].tokens_after_start, 3)
 
-        # Step 4: Budget reached, last token not NEW_LINE. Should force NEW_LINE_TOKEN_ID.
-        logits = self._get_initial_logits(1)
-        processor.update_state(mock_runner.share_inputs)
-        processed_logits = processor.apply(logits)
-
-        # Verify all other logits are -inf, only NEW_LINE_TOKEN_ID is 0.0
-        # Use <= for comparison because paddle.full creates -10.0 and comparison can have precision issues
-        other_logits = paddle.concat(
-            [
-                processed_logits[0, :line_break_id],
-                processed_logits[0, line_break_id + 1 : VOCAB_SIZE],
-            ],
-            axis=0,
-        )
-        self.assertTrue(paddle.all(other_logits <= -10.0).item() or paddle.all(other_logits == -float("inf")).item())
-        self.assertEqual(processed_logits[0, line_break_id].item(), 0.0)
-
-        next_token = mock_runner.generate_next_token(processed_logits)[0]
-        self.assertEqual(next_token, line_break_id)  # Forces NEW_LINE
-        mock_runner.update_request_state(0, mock_req, pre_id=next_token)
-        processor.update_state(mock_runner.share_inputs)
-        self.assertEqual(processor._states[req_id].tokens_after_start, 4)  # Still increment
-        self.assertEqual(processor._states[req_id].last_token_id, line_break_id)
-
-        # Step 5: Last token is NEW_LINE. Should force THINKING_END_TOKEN_ID.
+        # Step 5: Budget reached, should force THINKING_END_TOKEN_ID directly.
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)
         processed_logits = processor.apply(logits)
@@ -324,9 +328,9 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         self.assertEqual(next_token, stop_sentence_token_ids[0])
 
     def test_thinking_budget_no_stop_sentence_defaults(self):
-        # Scenario: No stop sentence, budget reached should force newline.
+        # Scenario: No stop sentence, budget reached should force thinking_end directly.
         req_id = "test_req_no_stop_sentence"
-        prompt_ids = [THINKING_START_TOKEN_ID, 42]  # 1 token after start
+        prompt_ids = [THINKING_START_TOKEN_ID, 42]
         sampling_params = SamplingParams(logits_processors_args={"thinking_budget": 1})
         mock_req = MockRequest(req_id, prompt_ids, sampling_params)
 
@@ -334,35 +338,42 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         mock_runner.update_request_state(0, mock_req, pre_id=prompt_ids[-1])
 
         processor = ThinkingBudgetLogitsProcessor(self.fd_config)
-        line_break_id = processor.line_break_token_id
 
         processor.update_state(mock_runner.share_inputs)
         logits = self._get_initial_logits(1)
         processed_logits = processor.apply(logits)
         next_token = mock_runner.generate_next_token(processed_logits)[0]
-        self.assertEqual(next_token, line_break_id)
+        self.assertEqual(next_token, 0)
+        mock_runner.update_request_state(0, mock_req, pre_id=next_token)
+        processor.update_state(mock_runner.share_inputs)
+
+        logits = self._get_initial_logits(1)
+        processor.update_state(mock_runner.share_inputs)
+        processed_logits = processor.apply(logits)
+        next_token = mock_runner.generate_next_token(processed_logits)[0]
+        self.assertEqual(next_token, THINKING_END_TOKEN_ID)
 
     def test_thinking_budget_uses_config_token_ids(self):
         # Scenario: Processor should use token ids from model config.
         self.fd_config.model_config.think_start_id = 123
         self.fd_config.model_config.think_end_id = 124
-        self.fd_config.model_config.line_break_id = 125
+        self.fd_config.model_config.line_break_id = -1
         processor = ThinkingBudgetLogitsProcessor(self.fd_config)
         self.assertEqual(processor.think_start_token_id, 123)
         self.assertEqual(processor.think_end_token_id, 124)
-        self.assertEqual(processor.line_break_token_id, 125)
+        self.assertEqual(processor.line_break_token_id, -1)
         self.assertTrue(processor._enabled)
 
     def test_thinking_budget_disabled_when_token_ids_missing(self):
         # Scenario: Processor should be disabled when token ids are not configured.
         self.fd_config.model_config.think_start_id = -1
         self.fd_config.model_config.think_end_id = -1
-        self.fd_config.model_config.line_break_id = -1
+        self.fd_config.model_config.line_break_id = NEW_LINE_TOKEN_ID
         processor = ThinkingBudgetLogitsProcessor(self.fd_config)
         self.assertFalse(processor._enabled)
         self.assertEqual(processor.think_start_token_id, -1)
         self.assertEqual(processor.think_end_token_id, -1)
-        self.assertEqual(processor.line_break_token_id, -1)
+        self.assertEqual(processor.line_break_token_id, NEW_LINE_TOKEN_ID)
 
         # update_state and apply should be no-op when disabled
         mock_runner = MockModelRunner(self.fd_config, max_num_seqs=1)
@@ -493,6 +504,28 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         self.assertEqual(state.tokens_after_start, 2)
         self.assertEqual(state.last_token_id, 99)
 
+    def test_thinking_budget_prompt_state_from_token_ids_all_fallback(self):
+        req_id = "req_gpu_fallback"
+        mock_runner = MockModelRunner(self.fd_config, max_num_seqs=1)
+        mock_runner.share_inputs["req_ids"][0] = req_id
+        mock_runner.share_inputs["logits_processors_args"][0] = {"thinking_budget": 3}
+        mock_runner.share_inputs["prompt_ids"] = None
+        mock_runner.share_inputs["token_ids_all"][0, :4] = paddle.to_tensor(
+            [1, THINKING_START_TOKEN_ID, 2, 3], dtype=paddle.int64
+        )
+        mock_runner.share_inputs["prompt_lens"][0, 0] = paddle.to_tensor(4, dtype=paddle.int64)
+        mock_runner.share_inputs["next_tokens"][0, 0] = paddle.to_tensor(-1, dtype=paddle.int64)
+
+        processor = ThinkingBudgetLogitsProcessor(self.fd_config)
+        processor.update_state(mock_runner.share_inputs)
+
+        state = processor._states[req_id]
+        self.assertTrue(state.prompt_checked)
+        self.assertTrue(state.started)
+        self.assertFalse(state.ended)
+        self.assertEqual(state.tokens_after_start, 0)
+        self.assertEqual(state.last_token_id, 3)
+
     def test_thinking_budget_not_configured(self):
         # Scenario: Processor is active, but request does not provide thinking_budget
         req_id = "test_req_3"
@@ -532,20 +565,7 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         self.assertEqual(processor._states[req_id].tokens_after_start, 0)  # No tokens after start yet
         self.assertEqual(processor._states[req_id].last_token_id, THINKING_START_TOKEN_ID)
 
-        # Step 1: Budget 0 reached, last token is THINKING_START. Should force NEW_LINE.
-        logits = self._get_initial_logits(1)
-        processor.update_state(mock_runner.share_inputs)
-        processed_logits = processor.apply(logits)
-
-        self.assertEqual(processed_logits[0, NEW_LINE_TOKEN_ID].item(), 0.0)
-        next_token = mock_runner.generate_next_token(processed_logits)[0]
-        self.assertEqual(next_token, NEW_LINE_TOKEN_ID)
-        mock_runner.update_request_state(0, mock_req, pre_id=next_token)
-        processor.update_state(mock_runner.share_inputs)
-        self.assertEqual(processor._states[req_id].tokens_after_start, 1)  # Still increments
-        self.assertEqual(processor._states[req_id].last_token_id, NEW_LINE_TOKEN_ID)
-
-        # Step 2: Last token is NEW_LINE. Should force THINKING_END_TOKEN_ID.
+        # Step 1: Budget 0 reached, last token is THINKING_START. Should force THINKING_END.
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)
         processed_logits = processor.apply(logits)
@@ -572,7 +592,7 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         processor.update_state(mock_runner.share_inputs)
         self.assertTrue(processor._states[req_id].started)
         self.assertTrue(processor._states[req_id].ended)
-        self.assertEqual(processor._states[req_id].tokens_after_start, 2)  # Tokens 2, THINKING_END after start
+        self.assertEqual(processor._states[req_id].tokens_after_start, 0)
 
         logits = self._get_initial_logits(1)
         processor.update_state(mock_runner.share_inputs)
@@ -582,7 +602,7 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
     def test_multiple_requests(self):
         # Scenario: Multiple requests with different thinking states
         req_id_1 = "req_a"
-        prompt_ids_1 = [THINKING_START_TOKEN_ID, 10, 11]  # budget 2, last_token=11, tokens_after_start=2
+        prompt_ids_1 = [THINKING_START_TOKEN_ID, 10, 11]
         sampling_params_1 = SamplingParams(logits_processors_args={"thinking_budget": 2})
         mock_req_1 = MockRequest(req_id_1, prompt_ids_1, sampling_params_1)
 
@@ -592,7 +612,7 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         mock_req_2 = MockRequest(req_id_2, prompt_ids_2, sampling_params_2)
 
         req_id_3 = "req_c"
-        prompt_ids_3 = [THINKING_START_TOKEN_ID, 30]  # budget 1, last_token=30, tokens_after_start=1
+        prompt_ids_3 = [THINKING_START_TOKEN_ID, 30]
         sampling_params_3 = SamplingParams(logits_processors_args={"thinking_budget": 1})
         mock_req_3 = MockRequest(req_id_3, prompt_ids_3, sampling_params_3)
 
@@ -609,14 +629,14 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         # Verify initial states
         self.assertTrue(processor._states[req_id_1].started)
         self.assertFalse(processor._states[req_id_1].ended)
-        self.assertEqual(processor._states[req_id_1].tokens_after_start, 2)
+        self.assertEqual(processor._states[req_id_1].tokens_after_start, 0)
         self.assertEqual(processor._states[req_id_1].last_token_id, 11)
 
         self.assertNotIn(req_id_2, processor._states)  # No budget specified for req_2
 
         self.assertTrue(processor._states[req_id_3].started)
         self.assertFalse(processor._states[req_id_3].ended)
-        self.assertEqual(processor._states[req_id_3].tokens_after_start, 1)
+        self.assertEqual(processor._states[req_id_3].tokens_after_start, 0)
         self.assertEqual(processor._states[req_id_3].last_token_id, 30)
 
         # Simulate logits for the batch
@@ -624,16 +644,14 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         processor.update_state(mock_runner.share_inputs)  # Ensure state is updated before apply
         processed_batch_logits = processor.apply(batch_logits)
 
-        # Req 1: budget 2, tokens_after_start 2. Should force NEW_LINE (last_token_id is 11, not NEW_LINE)
-        self.assertEqual(processed_batch_logits[0, NEW_LINE_TOKEN_ID].item(), 0.0)
-        self.assertEqual(paddle.argmax(processed_batch_logits[0], axis=-1).item(), NEW_LINE_TOKEN_ID)
+        # Req 1: prompt-side content does not consume budget, so first step is normal generation.
+        self.assertEqual(paddle.argmax(processed_batch_logits[0], axis=-1).item(), 0)
 
         # Req 2: No thinking budget, normal generation
         self.assertEqual(paddle.argmax(processed_batch_logits[1], axis=-1).item(), 0)
 
-        # Req 3: budget 1, tokens_after_start 1. Should force NEW_LINE (last_token_id is 30, not NEW_LINE)
-        self.assertEqual(processed_batch_logits[2, NEW_LINE_TOKEN_ID].item(), 0.0)
-        self.assertEqual(paddle.argmax(processed_batch_logits[2], axis=-1).item(), NEW_LINE_TOKEN_ID)
+        # Req 3: prompt-side content does not consume budget, so first step is normal generation.
+        self.assertEqual(paddle.argmax(processed_batch_logits[2], axis=-1).item(), 0)
 
         # Simulate generating next tokens and updating state
         next_tokens = mock_runner.generate_next_token(processed_batch_logits)
@@ -643,20 +661,25 @@ class TestThinkingBudgetLogitsProcessor(unittest.TestCase):
         processor.update_state(mock_runner.share_inputs)
 
         # Verify updated states for next step
-        self.assertEqual(processor._states[req_id_1].last_token_id, NEW_LINE_TOKEN_ID)
-        self.assertEqual(processor._states[req_id_3].last_token_id, NEW_LINE_TOKEN_ID)
+        self.assertEqual(processor._states[req_id_1].last_token_id, 0)
+        self.assertEqual(processor._states[req_id_3].last_token_id, 0)
+        self.assertEqual(processor._states[req_id_1].tokens_after_start, 1)
+        self.assertEqual(processor._states[req_id_3].tokens_after_start, 1)
+        self.assertFalse(processor._states[req_id_1].ended)
+        self.assertFalse(processor._states[req_id_3].ended)
 
         batch_logits = self._get_initial_logits(3)
         processor.update_state(mock_runner.share_inputs)
         processed_batch_logits = processor.apply(batch_logits)
 
-        # Req 1: last token was NEW_LINE. Should force THINKING_END
-        self.assertEqual(paddle.argmax(processed_batch_logits[0], axis=-1).item(), THINKING_END_TOKEN_ID)
+        # Req 1: budget 2, tokens_after_start 1. Still normal generation.
+        self.assertEqual(paddle.argmax(processed_batch_logits[0], axis=-1).item(), 0)
 
         # Req 2: Still normal generation
         self.assertEqual(paddle.argmax(processed_batch_logits[1], axis=-1).item(), 0)
 
-        # Req 3: last token was NEW_LINE. Should force THINKING_END
+        # Req 3: budget 1 reached after one generated token, should now force THINKING_END.
+        self.assertEqual(processed_batch_logits[2, THINKING_END_TOKEN_ID].item(), 0.0)
         self.assertEqual(paddle.argmax(processed_batch_logits[2], axis=-1).item(), THINKING_END_TOKEN_ID)
 
 
@@ -724,7 +747,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         self.assertTrue(updated["think_prompt_checked"])
         self.assertTrue(updated["think_prompt_started"])
         self.assertTrue(updated["think_prompt_ended"])
-        self.assertEqual(updated["think_prompt_tokens_after_start"], 2)
+        self.assertEqual(updated["think_prompt_tokens_after_start"], 0)
         self.assertEqual(updated["think_prompt_last_token_id"], 3)
 
     def test_v1_process_request_missing_logits_processors_args(self):
@@ -833,6 +856,58 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         self.assertEqual(processor.encode_with_cache("iter"), [41, 42])
         self.assertNotIn(("np", False), processor._tokenize_cache)
 
+    def test_text_encode_with_cache_lazy_init(self):
+        processor = TextDataProcessor.__new__(TextDataProcessor)
+        call_counter = {"count": 0}
+
+        def _text2ids(text, max_model_len=None, add_special_tokens=False):
+            call_counter["count"] += 1
+            return np.array([51, 52], dtype=np.int64)
+
+        processor.text2ids = _text2ids
+
+        self.assertFalse(hasattr(processor, "_tokenize_cache"))
+        self.assertEqual(processor.encode_with_cache("lazy"), [51, 52])
+        self.assertTrue(hasattr(processor, "_tokenize_cache"))
+        self.assertEqual(processor.encode_with_cache("lazy"), [51, 52])
+        self.assertEqual(call_counter["count"], 1)
+
+    def test_v1_encode_with_cache_lazy_init(self):
+        processor = V1TextDataProcessor.__new__(V1TextDataProcessor)
+        call_counter = {"count": 0}
+
+        def _text2ids(text, max_model_len=None, add_special_tokens=False):
+            call_counter["count"] += 1
+            return np.array([61, 62], dtype=np.int64)
+
+        processor.text2ids = _text2ids
+
+        self.assertFalse(hasattr(processor, "_tokenize_cache"))
+        self.assertEqual(processor.encode_with_cache("lazy"), [61, 62])
+        self.assertTrue(hasattr(processor, "_tokenize_cache"))
+        self.assertEqual(processor.encode_with_cache("lazy"), [61, 62])
+        self.assertEqual(call_counter["count"], 1)
+
+    def test_ernie_encode_literal_text_with_cache(self):
+        processor = ErnieTextDataProcessor.__new__(ErnieTextDataProcessor)
+        processor.tokenizer = SimpleNamespace(
+            tokenize=lambda text: ["token_a", "token_b"],
+            convert_tokens_to_ids=lambda tokens: [71, 72],
+        )
+
+        self.assertEqual(processor._encode_literal_text_with_cache("fallback"), [71, 72])
+        self.assertEqual(processor._encode_literal_text_with_cache("fallback"), [71, 72])
+
+    def test_v1_ernie_encode_literal_text_with_cache(self):
+        processor = V1ErnieTextDataProcessor.__new__(V1ErnieTextDataProcessor)
+        processor.tokenizer = SimpleNamespace(
+            tokenize=lambda text: ["token_c", "token_d"],
+            convert_tokens_to_ids=lambda tokens: [81, 82],
+        )
+
+        self.assertEqual(processor._encode_literal_text_with_cache("fallback"), [81, 82])
+        self.assertEqual(processor._encode_literal_text_with_cache("fallback"), [81, 82])
+
     def test_text_update_thinking_prompt_state_branches(self):
         processor = TextDataProcessor.__new__(TextDataProcessor)
         processor._think_token_ids = None
@@ -868,7 +943,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         )
         self.assertTrue(with_start_no_end["think_prompt_started"])
         self.assertFalse(with_start_no_end["think_prompt_ended"])
-        self.assertEqual(with_start_no_end["think_prompt_tokens_after_start"], 2)
+        self.assertEqual(with_start_no_end["think_prompt_tokens_after_start"], 0)
         self.assertEqual(with_start_no_end["think_prompt_last_token_id"], 3)
 
         # 命中 _get_think_token_ids 的缓存分支
@@ -891,7 +966,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         )
         self.assertTrue(with_start_no_end["think_prompt_started"])
         self.assertFalse(with_start_no_end["think_prompt_ended"])
-        self.assertEqual(with_start_no_end["think_prompt_tokens_after_start"], 2)
+        self.assertEqual(with_start_no_end["think_prompt_tokens_after_start"], 0)
         self.assertEqual(with_start_no_end["think_prompt_last_token_id"], 3)
 
         # 命中 _get_think_token_ids 的缓存分支
@@ -903,7 +978,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         processor.eos_token_ids = [1]
         processor.update_stop_seq = lambda *args, **kwargs: None
         processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
-        processor.encode_with_cache = lambda text, *args, **kwargs: [23] if text == "\n" else [201, 202]
+        processor._encode_literal_text_with_cache = lambda text: [201, 202]
         processor._update_thinking_prompt_state = lambda prompt_token_ids, args: args
         processor.reasoning_parser = None
 
@@ -924,7 +999,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
             processed = processor.process_request_dict(request, max_model_len=16)
         self.assertEqual(
             processed["logits_processors_args"].get("think_stop_sentence_token_ids"),
-            [23, 201, 202],
+            [201, 202],
         )
         self.assertNotIn("think_stop_sentence", processed["logits_processors_args"])
 
@@ -934,7 +1009,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         processor.eos_token_ids = [1]
         processor.update_stop_seq = lambda *args, **kwargs: None
         processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
-        processor.encode_with_cache = lambda text, *args, **kwargs: [23] if text == "\n" else [301, 302]
+        processor._encode_literal_text_with_cache = lambda text: [301, 302]
         processor._update_thinking_prompt_state = lambda prompt_token_ids, args: args
         processor.reasoning_parser = None
 
@@ -955,7 +1030,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
             processed = processor.process_request(request, max_model_len=16)
         self.assertEqual(
             processed.logits_processors_args.get("think_stop_sentence_token_ids"),
-            [23, 301, 302],
+            [301, 302],
         )
         self.assertNotIn("think_stop_sentence", processed.logits_processors_args)
 
@@ -965,7 +1040,7 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
         processor.eos_token_ids = [1]
         processor.update_stop_seq = lambda *args, **kwargs: None
         processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
-        processor.encode_with_cache = lambda text, *args, **kwargs: [23] if text == "\n" else [401, 402]
+        processor._encode_literal_text_with_cache = lambda text: [401, 402]
         processor._update_thinking_prompt_state = lambda prompt_token_ids, args: args
         processor.reasoning_parser = None
 
@@ -992,10 +1067,173 @@ class TestThinkingBudgetSupplemental(unittest.TestCase):
             processed = processor.process_request_dict(request, max_model_len=16)
         self.assertEqual(
             processed.sampling_params.logits_processors_args.get("think_stop_sentence_token_ids"),
-            [23, 401, 402],
+            [401, 402],
         )
         self.assertNotIn("think_stop_sentence", processed.sampling_params.logits_processors_args)
 
+    def test_ernie_process_request_dict_prepares_thinking_budget_args(self):
+        processor = ErnieTextDataProcessor.__new__(ErnieTextDataProcessor)
+        processor._apply_default_parameters = lambda request: request
+        processor.eos_token_ids = [1]
+        processor.update_stop_seq = lambda *args, **kwargs: None
+        processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
+        processor._encode_literal_text_with_cache = lambda text: [501, 502]
+        processor.tokenizer = DummyTokenizerForTextProcessor()
+        processor.reasoning_parser = None
+
+        request = {
+            "request_id": "req_ernie_text",
+            "eos_token_ids": [1],
+            "prompt_token_ids": [1, THINKING_START_TOKEN_ID, 2],
+            "prompt": None,
+            "messages": None,
+            "logits_processors_args": {"thinking_budget": 20, "think_stop_sentence": "done"},
+            "bad_words": None,
+            "bad_words_token_ids": None,
+            "max_tokens": 1,
+            "temperature": 1.0,
+            "top_p": 0.9,
+            "response_max_tokens": None,
+            "enable_thinking": True,
+        }
+        with patch("fastdeploy.input.ernie4_5_processor.process_stop_token_ids", lambda *args, **kwargs: None):
+            processed = processor.process_request_dict(request, max_model_len=16)
+
+        self.assertEqual(processed["logits_processors_args"]["think_stop_sentence_token_ids"], [501, 502])
+        self.assertTrue(processed["logits_processors_args"]["think_prompt_started"])
+        self.assertFalse(processed["logits_processors_args"]["think_prompt_ended"])
+        self.assertEqual(processed["logits_processors_args"]["think_prompt_tokens_after_start"], 0)
+
+    def test_v1_ernie_process_request_dict_prepares_thinking_budget_args(self):
+        processor = V1ErnieTextDataProcessor.__new__(V1ErnieTextDataProcessor)
+        processor._apply_default_parameters = lambda request: request
+        processor.eos_token_ids = [1]
+        processor.update_stop_seq = lambda *args, **kwargs: None
+        processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
+        processor._encode_literal_text_with_cache = lambda text: [601, 602]
+        processor.tokenizer = DummyTokenizerForTextProcessor()
+        processor.reasoning_parser = None
+
+        request = DummyRequestV1(
+            request_id="req_v1_ernie_text",
+            eos_token_ids=[1],
+            prompt_token_ids=[1, THINKING_START_TOKEN_ID, 2],
+            prompt=None,
+            messages=None,
+            chat_template_kwargs=None,
+            enable_thinking=True,
+            sampling_params=SimpleNamespace(
+                bad_words=None,
+                bad_words_token_ids=None,
+                max_tokens=1,
+                temperature=1.0,
+                top_p=0.9,
+                repetition_penalty=1.0,
+                frequency_penalty=0.0,
+                presence_penalty=0.0,
+                response_max_tokens=None,
+                n=1,
+                logits_processors_args={"thinking_budget": 20, "think_stop_sentence": "done"},
+            ),
+        )
+        with patch("fastdeploy.input.v1.ernie4_5_processor.process_stop_token_ids", lambda *args, **kwargs: None):
+            processed = processor.process_request_dict(request, max_model_len=16)
+
+        self.assertEqual(processed.sampling_params.logits_processors_args["think_stop_sentence_token_ids"], [601, 602])
+        self.assertTrue(processed.sampling_params.logits_processors_args["think_prompt_started"])
+        self.assertFalse(processed.sampling_params.logits_processors_args["think_prompt_ended"])
+        self.assertEqual(processed.sampling_params.logits_processors_args["think_prompt_tokens_after_start"], 0)
+
+    def test_ernie_vl_process_request_dict_prepares_thinking_budget_args(self):
+        processor = ErnieVLDataProcessor.__new__(ErnieVLDataProcessor)
+        processor._apply_default_parameters = lambda request: request
+        processor.eos_token_ids = [1]
+        processor.update_stop_seq = lambda *args, **kwargs: None
+        processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
+        processor._encode_literal_text_with_cache = lambda text: [701, 702]
+        processor.tokenizer = DummyTokenizerForTextProcessor()
+        processor.reasoning_parser = None
+        processor._check_mm_limits = lambda *args, **kwargs: None
+        processor.append_completion_tokens = lambda *args, **kwargs: None
+        processor.pack_outputs = lambda outs: outs
+        processor.ernie4_5_processor = SimpleNamespace(
+            request2ids=lambda request: {"input_ids": np.array([1, THINKING_START_TOKEN_ID, 2], dtype=np.int64)}
+        )
+
+        request = {
+            "request_id": "req_ernie_vl",
+            "eos_token_ids": [1],
+            "messages": [{"role": "user", "content": "hi"}],
+            "bad_words": None,
+            "bad_words_token_ids": None,
+            "logits_processors_args": {"thinking_budget": 20, "think_stop_sentence": "done"},
+            "max_tokens": 1,
+            "top_p": 0.9,
+            "response_max_tokens": None,
+        }
+        with patch(
+            "fastdeploy.input.ernie4_5_vl_processor.ernie4_5_vl_processor.process_stop_token_ids",
+            lambda *args, **kwargs: None,
+        ):
+            processed = processor.process_request_dict(request, max_model_len=16)
+
+        self.assertEqual(processed["logits_processors_args"]["think_stop_sentence_token_ids"], [701, 702])
+        self.assertTrue(processed["logits_processors_args"]["think_prompt_started"])
+        self.assertFalse(processed["logits_processors_args"]["think_prompt_ended"])
+        self.assertEqual(processed["logits_processors_args"]["think_prompt_tokens_after_start"], 0)
+
+    def test_v1_ernie_vl_process_request_dict_prepares_thinking_budget_args(self):
+        processor = V1ErnieVLDataProcessor.__new__(V1ErnieVLDataProcessor)
+        processor._apply_default_parameters = lambda request: request
+        processor.eos_token_ids = [1]
+        processor.update_stop_seq = lambda *args, **kwargs: None
+        processor.update_bad_words = lambda bad_words, bad_words_token_ids: bad_words_token_ids
+        processor._encode_literal_text_with_cache = lambda text: [801, 802]
+        processor.tokenizer = DummyTokenizerForTextProcessor()
+        processor.reasoning_parser = None
+        processor._check_mm_limits = lambda *args, **kwargs: None
+        processor.append_completion_tokens = lambda *args, **kwargs: None
+        processor.pack_outputs = lambda outs: outs
+        processor.ernie4_5_processor = SimpleNamespace(
+            request2ids=lambda request: {"input_ids": np.array([1, THINKING_START_TOKEN_ID, 2], dtype=np.int64)}
+        )
+
+        request = DummyRequestV1(
+            request_id="req_v1_ernie_vl",
+            eos_token_ids=[1],
+            prompt_token_ids=None,
+            prompt=None,
+            messages=[{"role": "user", "content": "hi"}],
+            chat_template_kwargs=None,
+            enable_thinking=True,
+            completion_token_ids=None,
+            multimodal_data=None,
+            sampling_params=SimpleNamespace(
+                bad_words=None,
+                bad_words_token_ids=None,
+                max_tokens=1,
+                temperature=1.0,
+                top_p=0.9,
+                repetition_penalty=1.0,
+                frequency_penalty=0.0,
+                presence_penalty=0.0,
+                response_max_tokens=None,
+                reasoning_max_tokens=None,
+                n=1,
+                logits_processors_args={"thinking_budget": 20, "think_stop_sentence": "done"},
+            ),
+        )
+        with patch(
+            "fastdeploy.input.v1.ernie4_5_vl_processor.ernie4_5_vl_processor.process_stop_token_ids",
+            lambda *args, **kwargs: None,
+        ):
+            processed = processor.process_request_dict(request, max_model_len=16)
+
+        self.assertEqual(processed.sampling_params.logits_processors_args["think_stop_sentence_token_ids"], [801, 802])
+        self.assertTrue(processed.sampling_params.logits_processors_args["think_prompt_started"])
+        self.assertFalse(processed.sampling_params.logits_processors_args["think_prompt_ended"])
+        self.assertEqual(processed.sampling_params.logits_processors_args["think_prompt_tokens_after_start"], 0)
+
 
 if __name__ == "__main__":
     unittest.main()