[OP][Feature] 统一 limit_thinking_content_length CUDA 算子，支持回复长度限制与注入序列 (#6493)

* Initial plan

* Migrate PRs #6311, #6129, #6305 to develop and merge unit tests

Co-authored-by: yuanlehome <23653004+yuanlehome@users.noreply.github.com>

* fix

* update

* fix

* fix ci

* fix ci

* Initial plan

* test: add test_chat_with_response_max_tokens to test_EB_VL_Lite_serving.py

Co-authored-by: yuanlehome <23653004+yuanlehome@users.noreply.github.com>

* test: add disable-thinking case to test_chat_with_response_max_tokens

Co-authored-by: yuanlehome <23653004+yuanlehome@users.noreply.github.com>

* test: add both reasoning_max_tokens and response_max_tokens case

Co-authored-by: yuanlehome <23653004+yuanlehome@users.noreply.github.com>

* fix ci

* fix ci

* fix ci

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: yuanlehome <23653004+yuanlehome@users.noreply.github.com>

fastdeploy/model_executor/pre_and_post_process.py

@@ -32,8 +32,7 @@ from fastdeploy.worker.input_batch import (
 if current_platform.is_iluvatar():
     from fastdeploy.model_executor.ops.iluvatar import (
         get_padding_offset,
-        limit_thinking_content_length_v1,
-        limit_thinking_content_length_v2,
+        limit_thinking_content_length,
         save_output,
         set_stop_value_multi_ends,
         step_paddle,
@@ -58,14 +57,12 @@ elif current_platform.is_dcu():
 elif current_platform.is_maca():
     from fastdeploy.model_executor.ops.gpu import (
         get_padding_offset,
-        limit_thinking_content_length_v1,
-        limit_thinking_content_length_v2,
+        limit_thinking_content_length,
         save_output,
         save_output_topk,
         set_stop_value_multi_ends,
         speculate_get_seq_lens_output,
-        speculate_limit_thinking_content_length_v1,
-        speculate_limit_thinking_content_length_v2,
+        speculate_limit_thinking_content_length,
         speculate_save_output,
         speculate_save_output_topk,
         speculate_set_stop_value_multi_seqs,
@@ -102,10 +99,8 @@ else:
         step_reschedule,
         update_inputs_v1,
         speculate_step_reschedule,
-        limit_thinking_content_length_v1,
-        limit_thinking_content_length_v2,
-        speculate_limit_thinking_content_length_v1,
-        speculate_limit_thinking_content_length_v2,
+        limit_thinking_content_length,
+        speculate_limit_thinking_content_length,
     )
 
 from fastdeploy.model_executor.entropy_utils import (
@@ -120,85 +115,6 @@ from fastdeploy.worker.output import LogprobsTensors, ModelOutputData, SamplerOu
 DISABLE_RECOVER = envs.FD_DISABLED_RECOVER == "1"
 
 
-def limit_thinking_content_length(
-    limit_strategy: str,
-    sampled_token_ids: paddle.Tensor,
-    max_think_lens: paddle.Tensor,
-    step_idx: paddle.Tensor,
-    limit_think_status: paddle.Tensor,
-    stop_flags: paddle.Tensor,
-    eos_token_ids: paddle.Tensor,
-    think_end_id: int,
-    line_break_id: int = None,
-):
-    if limit_strategy == "</think>":
-        # for ernie-45-vl
-        limit_thinking_content_length_v1(
-            sampled_token_ids,
-            max_think_lens,
-            step_idx,
-            limit_think_status,
-            stop_flags,
-            eos_token_ids,  # Address the issue where the model outputs EOS tokens during the reasoning process due to model performance problems
-            think_end_id,
-        )
-    elif limit_strategy == "\n</think>\n\n":
-        # for ernie-x1
-        assert line_break_id > 0
-        limit_thinking_content_length_v2(
-            sampled_token_ids,
-            max_think_lens,
-            step_idx,
-            limit_think_status,
-            stop_flags,
-            think_end_id,
-            line_break_id,
-        )
-    else:
-        raise NotImplementedError(f"Not support {limit_strategy=} for limit thinking content length.")
-
-
-def speculate_limit_thinking_content_length(
-    limit_strategy: str,
-    accept_tokens: paddle.Tensor,
-    max_think_lens: paddle.Tensor,
-    step_idx: paddle.Tensor,
-    limit_think_status: paddle.Tensor,
-    accept_num: paddle.Tensor,
-    stop_flags: paddle.Tensor,
-    eos_token_ids: paddle.Tensor,
-    think_end_id: int,
-    line_break_id: int = None,
-):
-    if limit_strategy == "</think>":
-        # for ernie-45-vl
-        speculate_limit_thinking_content_length_v1(
-            accept_tokens,
-            max_think_lens,
-            step_idx,
-            limit_think_status,
-            accept_num,
-            stop_flags,
-            eos_token_ids,  # Address the issue where the model outputs EOS tokens during the reasoning process due to model performance problems
-            think_end_id,
-        )
-    elif limit_strategy == "\n</think>\n\n":
-        # for ernie-x1
-        assert line_break_id > 0
-        speculate_limit_thinking_content_length_v2(
-            accept_tokens,
-            max_think_lens,
-            step_idx,
-            limit_think_status,
-            accept_num,
-            stop_flags,
-            think_end_id,
-            line_break_id,
-        )
-    else:
-        raise NotImplementedError(f"Not support {limit_strategy=} for limit thinking content length.")
-
-
 def pre_process(
     token_num_cpu: int,
     input_ids: paddle.Tensor,
@@ -321,21 +237,22 @@ def post_process_normal(
     sampling_metadata: SamplingMetadata,
     block_size: int = 64,
     think_end_id: int = -1,
-    line_break_id: int = -1,
+    splitwise_role_is_decode: bool = False,
     enable_entropy: bool = False,
 ):
     """Post-processing steps after completing a single token generation."""
     if think_end_id > 0:
         limit_thinking_content_length(
-            limit_strategy=envs.FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR,
-            sampled_token_ids=sampler_output.sampled_token_ids,
-            max_think_lens=share_inputs["max_think_lens"],
-            step_idx=share_inputs["step_idx"],
-            limit_think_status=share_inputs["limit_think_status"],
-            stop_flags=share_inputs["stop_flags"],
-            eos_token_ids=share_inputs["eos_token_id"],
-            think_end_id=think_end_id,
-            line_break_id=line_break_id,
+            sampler_output.sampled_token_ids,
+            share_inputs["max_think_lens"],
+            share_inputs["max_reply_lens"],
+            share_inputs["step_idx"],
+            share_inputs["limit_think_status"],
+            share_inputs["stop_flags"],
+            share_inputs["eos_token_id"],
+            share_inputs["inject_token_ids"],
+            think_end_id,
+            splitwise_role_is_decode,
         )
     # 1. Set stop value
     paddle.assign(
@@ -480,21 +397,22 @@ def post_process_specualate(
     save_each_rank: bool = False,
     skip_save_output: bool = False,
     think_end_id: int = -1,
-    line_break_id: int = -1,
+    splitwise_role_is_decode: bool = False,
     enable_entropy: bool = False,
 ):
     if think_end_id > 0:
         speculate_limit_thinking_content_length(
-            limit_strategy=envs.FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR,
-            accept_tokens=share_inputs["accept_tokens"],
-            max_think_lens=share_inputs["max_think_lens"],
-            step_idx=share_inputs["step_idx"],
-            limit_think_status=share_inputs["limit_think_status"],
-            accept_num=share_inputs["accept_num"],
-            stop_flags=share_inputs["stop_flags"],
-            eos_token_ids=share_inputs["eos_token_id"],
-            think_end_id=think_end_id,
-            line_break_id=line_break_id,
+            share_inputs["accept_tokens"],
+            share_inputs["max_think_lens"],
+            share_inputs["max_reply_lens"],
+            share_inputs["step_idx"],
+            share_inputs["limit_think_status"],
+            share_inputs["accept_num"],
+            share_inputs["stop_flags"],
+            share_inputs["eos_token_id"],
+            share_inputs["inject_token_ids"],
+            think_end_id,
+            splitwise_role_is_decode,
         )
     speculate_set_stop_value_multi_seqs(
         model_output.accept_tokens,
@@ -602,7 +520,7 @@ def post_process(
     skip_save_output: bool = False,
     async_output_queue: queue.Queue = None,
     think_end_id: int = -1,
-    line_break_id: int = -1,
+    splitwise_role_is_decode: bool = False,
     enable_entropy: bool = False,
 ) -> None:
     """Post-processing steps after completing a single token generation."""
@@ -627,7 +545,7 @@ def post_process(
                 save_each_rank,
                 skip_save_output,
                 think_end_id,
-                line_break_id,
+                splitwise_role_is_decode,
                 enable_entropy,
             )
         else:
@@ -638,7 +556,7 @@ def post_process(
                 sampling_metadata,
                 block_size,
                 think_end_id,
-                line_break_id,
+                splitwise_role_is_decode,
                 enable_entropy,
             )
             share_inputs["last_preempted_idx"].copy_(share_inputs["preempted_idx"])