[Feature] Support include_stop_str_in_output (#2919)

Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com>

fastdeploy/entrypoints/openai/serving_chat.py

@@ -104,6 +104,7 @@ class OpenAIServingChat:
         num_choices = 1
         max_streaming_response_tokens = 1
         enable_thinking = None
+        include_stop_str_in_output = False
         if request.metadata is not None and request.metadata.get("max_streaming_response_tokens", 1) > 1:
             max_streaming_response_tokens = request.metadata["max_streaming_response_tokens"]
 
@@ -152,8 +153,9 @@ class OpenAIServingChat:
                     raise ValueError("{}".format(res["error_msg"]))
                 if request.metadata is not None:
                     enable_thinking = request.metadata.get("enable_thinking")
+                    include_stop_str_in_output = request.metadata.get("include_stop_str_in_output", False)
                 self.engine_client.data_processor.process_response_dict(
-                    res, stream=True, enable_thinking=enable_thinking)
+                    res, stream=True, enable_thinking=enable_thinking, include_stop_str_in_output=include_stop_str_in_output)
 
                 if res['metrics']['first_token_time'] is not None:
                     arrival_time = res['metrics']['first_token_time']
@@ -282,6 +284,7 @@ class OpenAIServingChat:
         created_time = int(time.time())
         final_res = None
         enable_thinking = None
+        include_stop_str_in_output = False
         try:
             dealer = await aiozmq.create_zmq_stream(
                 zmq.DEALER,
@@ -312,8 +315,9 @@ class OpenAIServingChat:
                     raise ValueError("{}".format(data["error_msg"]))
                 if request.metadata is not None:
                     enable_thinking = request.metadata.get("enable_thinking")
+                    include_stop_str_in_output = request.metadata.get("include_stop_str_in_output", False)
                 data = self.engine_client.data_processor.process_response_dict(
-                    data, stream=False, enable_thinking=enable_thinking)
+                    data, stream=False, enable_thinking=enable_thinking, include_stop_str_in_output=include_stop_str_in_output)
                 # api_server_logger.debug(f"Client {request_id} received: {data}")
                 previous_num_tokens += len(data["outputs"]["token_ids"])
                 # The logprob for handling the response

fastdeploy/input/ernie_processor.py

@@ -100,7 +100,6 @@ class ErnieProcessor(BaseDataProcessor):
 
         if request.prompt_token_ids is None or len(
                 request.prompt_token_ids) == 0:
-            system = request.get("system")
             if request.prompt is None and request.messages is None:
                 raise ValueError(
                     f"The request should have `input_ids`, `text` or `messages`: {request}.")
@@ -149,7 +148,6 @@ class ErnieProcessor(BaseDataProcessor):
             request['stop_token_ids'] = stop_seqs
             request['stop_seqs_len'] = stop_seqs_len
 
-        system = request.get("system")
         # 处理prompt_token_ids
         if not request.get('prompt_token_ids'):
             if request.get('prompt') is None and request.get(
@@ -249,7 +247,7 @@ class ErnieProcessor(BaseDataProcessor):
         token_ids = response_dict["outputs"]["token_ids"]
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
-        if is_end and len(token_ids) > 0:
+        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
             if token_ids[-1] == self.tokenizer.eos_token_id:
                 token_ids = token_ids[:-1]
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
@@ -284,7 +282,7 @@ class ErnieProcessor(BaseDataProcessor):
         req_id = response_dict["request_id"]
         token_ids = response_dict["outputs"]["token_ids"]
 
-        if is_end and len(token_ids) > 0:
+        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
             if token_ids[-1] == self.tokenizer.eos_token_id:
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(

fastdeploy/input/text_processor.py

@@ -355,7 +355,7 @@ class DataProcessor(BaseDataProcessor):
         token_ids = response_dict["outputs"]["token_ids"]
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
-        if is_end and len(token_ids) > 0:
+        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
             if token_ids[-1] == self.tokenizer.eos_token_id:
                 token_ids = token_ids[:-1]
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
@@ -390,7 +390,7 @@ class DataProcessor(BaseDataProcessor):
         req_id = response_dict["request_id"]
         token_ids = response_dict["outputs"]["token_ids"]
 
-        if is_end and len(token_ids) > 0:
+        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
             if token_ids[-1] == self.tokenizer.eos_token_id:
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(
@@ -430,7 +430,7 @@ class DataProcessor(BaseDataProcessor):
                 response_dict, enable_thinking=enable_thinking, **kwargs)
         else:
             return self.process_response_dict_normal(
-                response_dict=response_dict, enable_thinking=enable_thinking)
+                response_dict=response_dict, enable_thinking=enable_thinking, **kwargs)
 
     def text2ids(self, text, max_model_len, raw_request=True):
         """

test/ci_use/EB_Lite/test_EB_Lite_serving.py

@@ -12,15 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pytest
-import requests
-import time
-import subprocess
-import socket
 import os
 import signal
+import socket
+import subprocess
 import sys
+import time
+
 import openai
+import pytest
+import requests
 
 # Read ports from environment variables; use default values if not set
 FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
@@ -313,4 +314,66 @@ def test_streaming(openai_client, capsys):
     output = []
     for chunk in response:
         output.append(chunk.choices[0].text)
-    assert len(output) > 0
+    assert len(output) > 0
+
+def test_non_streaming_with_stop_str(openai_client):
+    """
+    Test non-streaming chat functionality with the local service
+    """
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        metadata={"include_stop_str_in_output": True},
+        stream=False,
+    )
+    # Assertions to check the response structure
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert response.choices[0].message.content.endswith("</s>")
+
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        metadata={"include_stop_str_in_output": False},
+        stream=False,
+    )
+    # Assertions to check the response structure
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert not response.choices[0].message.content.endswith("</s>")
+
+def test_streaming_with_stop_str(openai_client):
+    """
+    Test non-streaming chat functionality with the local service
+    """
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        metadata={"include_stop_str_in_output": True},
+        stream=True,
+    )
+    # Assertions to check the response structure
+    last_token = ""
+    for chunk in response:
+        last_token = chunk.choices[0].delta.content
+    assert last_token == "</s>"
+
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        metadata={"include_stop_str_in_output": False},
+        stream=True,
+    )
+    # Assertions to check the response structure
+    last_token = ""
+    for chunk in response:
+        last_token = chunk.choices[0].delta.content
+    assert last_token != "</s>"