add Tool Parser (#3272)

* add tool-parser

* add tool-parser

* add tool parser

* add tool parser

* fix

* add offline

* add offline

* fix

* parsers:tool&reasoning

* 修改tool parser名称·

* update

* fix reasoning-parser

* add requirements

* fix finish reason

* fix

* fix reasoning-parser

* fix

* fix

* fix

* fix

* fix

---------

Co-authored-by: zhuzixuan <zhuzixuan@baidu.com>

fastdeploy/input/ernie_processor.py

@@ -42,7 +42,7 @@ class ErnieProcessor(BaseDataProcessor):
         pad_token_id (int): 存储填充符号的token ID。
     """
 
-    def __init__(self, model_name_or_path, reasoning_parser_obj=None):
+    def __init__(self, model_name_or_path, reasoning_parser_obj=None, tool_parser_obj=None):
 
         self.model_name_or_path = model_name_or_path
         data_processor_logger.info(f"model_name_or_path: {model_name_or_path}")
@@ -58,6 +58,7 @@ class ErnieProcessor(BaseDataProcessor):
             self.generation_config = None
 
         self.decode_status = dict()
+        self.tool_parsers = dict()
         self.thinking_parser_dict = dict()
         self._load_tokenizer()
         data_processor_logger.info(
@@ -71,6 +72,7 @@ class ErnieProcessor(BaseDataProcessor):
         self.eos_token_id_len = len(self.eos_token_ids)
         self.pad_token_id = self.get_pad_id()
         self.reasoning_parser = None
+        self.tool_parser_obj = tool_parser_obj
         if reasoning_parser_obj:
             self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
 
@@ -201,6 +203,12 @@ class ErnieProcessor(BaseDataProcessor):
             response_dict.outputs.reasoning_content = reasoning_content
         else:
             response_dict.outputs.text = full_text
+        if self.tool_parser_obj:
+            tool_parser = self.tool_parser_obj(self.tokenizer)
+            tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
+            if tool_call_info.tools_called:
+                response_dict.outputs.tool_calls = tool_call_info.tool_calls
+                response_dict.outputs.text = tool_call_info.content
         data_processor_logger.info(f"req_id:{req_id}, token)ids: {token_ids}")
         if response_dict.outputs.text == "" and response_dict.outputs.reasoning_content == "":
             return None
@@ -241,12 +249,20 @@ class ErnieProcessor(BaseDataProcessor):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            if enable_thinking and self.reasoning_parser:
+            if self.reasoning_parser and (
+                enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
+            ):
                 reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
                 response_dict["outputs"]["text"] = text
                 response_dict["outputs"]["reasoning_content"] = reasoning_content
             else:
                 response_dict["outputs"]["text"] = full_text
+            if self.tool_parser_obj:
+                tool_parser = self.tool_parser_obj(self.tokenizer)
+                tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
+                if tool_call_info.tools_called:
+                    response_dict["outputs"]["tool_call"] = tool_call_info.tool_calls
+                    response_dict["outputs"]["text"] = tool_call_info.content
             response_dict["outputs"]["raw_prediction"] = full_text
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
@@ -271,7 +287,9 @@ class ErnieProcessor(BaseDataProcessor):
             if token_ids[-1] == self.tokenizer.eos_token_id:
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
-        if enable_thinking and self.reasoning_parser:
+        if self.reasoning_parser and (
+            enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
+        ):
             reasoning_content, text = self.reasoning_parser.extract_reasoning_content_streaming(
                 previous_texts,
                 previous_texts + delta_text,
@@ -284,10 +302,25 @@ class ErnieProcessor(BaseDataProcessor):
             response_dict["outputs"]["reasoning_content"] = reasoning_content
         else:
             response_dict["outputs"]["text"] = delta_text
-        response_dict["outputs"]["raw_prediction"] = delta_text
+        if self.tool_parser_obj:
+            if req_id not in self.tool_parsers:
+                self.tool_parsers[req_id] = self.tool_parser_obj(self.tokenizer)
+            tool_parser = self.tool_parsers[req_id]
+            tool_call = tool_parser.extract_tool_calls_streaming(
+                previous_texts,
+                previous_texts + delta_text,
+                delta_text,
+                previous_token_ids,
+                previous_token_ids + token_ids,
+                token_ids,
+                response_dict,
+            )
+            response_dict["outputs"]["tool_delta_message"] = tool_call
         if is_end:
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
+            if req_id in self.tool_parsers:
+                del self.tool_parsers[req_id]
         return response_dict
 
     def messages2ids(self, request_or_messages):

fastdeploy/input/ernie_vl_processor.py

@@ -32,6 +32,7 @@ class ErnieMoEVLProcessor(ErnieProcessor):
         limit_mm_per_prompt=None,
         mm_processor_kwargs=None,
         reasoning_parser_obj=None,
+        tool_parser_obj=None,
     ):
         data_processor_logger.info(f"model_name_or_path: {model_name_or_path}")
         tokenizer_path = model_name_or_path
@@ -47,6 +48,7 @@ class ErnieMoEVLProcessor(ErnieProcessor):
         self.image_patch_id = self.ernie_processor.image_patch_id
         self.spatial_conv_size = self.ernie_processor.spatial_conv_size
 
+        self.tool_parsers = dict()
         self.decode_status = dict()
         self._load_tokenizer()
 
@@ -69,6 +71,7 @@ class ErnieMoEVLProcessor(ErnieProcessor):
         self.reasoning_parser = None
         if reasoning_parser_obj:
             self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
+        self.tool_parser_obj = tool_parser_obj
 
     def get_pad_id(self):
         """get pad id"""

fastdeploy/input/preprocess.py

@@ -18,6 +18,7 @@ from typing import Any, Dict, Optional
 
 from fastdeploy.config import ErnieArchitectures
 from fastdeploy.engine.config import ModelConfig
+from fastdeploy.entrypoints.openai.tool_parsers import ToolParserManager
 from fastdeploy.reasoning import ReasoningParserManager
 
 
@@ -48,6 +49,7 @@ class InputPreprocessor:
         limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
         mm_processor_kwargs: Optional[Dict[str, Any]] = None,
         enable_mm: bool = False,
+        tool_parser: str = None,
     ) -> None:
 
         self.model_name_or_path = model_name_or_path
@@ -55,6 +57,7 @@ class InputPreprocessor:
         self.enable_mm = enable_mm
         self.limit_mm_per_prompt = limit_mm_per_prompt
         self.mm_processor_kwargs = mm_processor_kwargs
+        self.tool_parser = tool_parser
 
     def create_processor(self):
         """
@@ -68,8 +71,11 @@ class InputPreprocessor:
             DataProcessor or MultiModalRegistry.Processor (Union[DataProcessor, MultiModalRegistry.Processor]): 数据处理器。
         """
         reasoning_parser_obj = None
+        tool_parser_obj = None
         if self.reasoning_parser:
             reasoning_parser_obj = ReasoningParserManager.get_reasoning_parser(self.reasoning_parser)
+        if self.tool_parser:
+            tool_parser_obj = ToolParserManager.get_tool_parser(self.tool_parser)
         architectures = ModelConfig({"model": self.model_name_or_path}).architectures[0]
         if not self.enable_mm:
             if not ErnieArchitectures.contains_ernie_arch(architectures):
@@ -78,6 +84,7 @@ class InputPreprocessor:
                 self.processor = DataProcessor(
                     model_name_or_path=self.model_name_or_path,
                     reasoning_parser_obj=reasoning_parser_obj,
+                    tool_parser_obj=tool_parser_obj,
                 )
             else:
                 from fastdeploy.input.ernie_processor import ErnieProcessor
@@ -85,6 +92,7 @@ class InputPreprocessor:
                 self.processor = ErnieProcessor(
                     model_name_or_path=self.model_name_or_path,
                     reasoning_parser_obj=reasoning_parser_obj,
+                    tool_parser_obj=tool_parser_obj,
                 )
         else:
             if not ErnieArchitectures.contains_ernie_arch(architectures):
@@ -97,5 +105,6 @@ class InputPreprocessor:
                     limit_mm_per_prompt=self.limit_mm_per_prompt,
                     mm_processor_kwargs=self.mm_processor_kwargs,
                     reasoning_parser_obj=reasoning_parser_obj,
+                    tool_parser_obj=tool_parser_obj,
                 )
         return self.processor

fastdeploy/input/text_processor.py

@@ -148,7 +148,7 @@ class BaseDataProcessor(ABC):
 
 
 class DataProcessor(BaseDataProcessor):
-    def __init__(self, model_name_or_path, reasoning_parser_obj=None):
+    def __init__(self, model_name_or_path, reasoning_parser_obj=None, tool_parser_obj=None):
         """
             Initializes the DecodeStatus object.
 
@@ -175,6 +175,7 @@ class DataProcessor(BaseDataProcessor):
             self.generation_config = None
 
         self.decode_status = dict()
+        self.tool_parsers = dict()
         self.tokenizer = self._load_tokenizer()
         data_processor_logger.info(
             f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, \
@@ -187,6 +188,7 @@ class DataProcessor(BaseDataProcessor):
         self.eos_token_id_len = len(self.eos_token_ids)
         self.pad_token_id = self.get_pad_id()
         self.reasoning_parser = None
+        self.tool_parser_obj = tool_parser_obj
         if reasoning_parser_obj:
             self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
         self.tokenizer.pad_token_id = self.pad_token_id
@@ -312,6 +314,12 @@ class DataProcessor(BaseDataProcessor):
         else:
             # 模型不支持思考,并且没单独设置enable_thinking为false
             response_dict.outputs.text = full_text
+        if self.tool_parser_obj:
+            tool_parser = self.tool_parser_obj(self.tokenizer)
+            tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
+            if tool_call_info.tools_called:
+                response_dict.outputs.tool_calls = tool_call_info.tool_calls
+                response_dict.outputs.text = tool_call_info.content
         data_processor_logger.info(f"req_id:{req_id}, token)ids: {token_ids}")
 
         return response_dict
@@ -343,6 +351,12 @@ class DataProcessor(BaseDataProcessor):
                 response_dict["outputs"]["reasoning_content"] = reasoning_content
             else:
                 response_dict["outputs"]["text"] = full_text
+            if self.tool_parser_obj:
+                tool_parser = self.tool_parser_obj(self.tokenizer)
+                tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
+                if tool_call_info.tools_called:
+                    response_dict["outputs"]["tool_call"] = tool_call_info.tool_calls
+                    response_dict["outputs"]["text"] = tool_call_info.content
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
         return response_dict
@@ -380,9 +394,25 @@ class DataProcessor(BaseDataProcessor):
             response_dict["outputs"]["reasoning_content"] = reasoning_content
         else:
             response_dict["outputs"]["text"] = delta_text
+        if self.tool_parser_obj and not is_end:
+            if req_id not in self.tool_parsers:
+                self.tool_parsers[req_id] = self.tool_parser_obj(self.tokenizer)
+            tool_parser = self.tool_parsers[req_id]
+            tool_call = tool_parser.extract_tool_calls_streaming(
+                previous_texts,
+                previous_texts + delta_text,
+                delta_text,
+                previous_token_ids,
+                previous_token_ids + token_ids,
+                token_ids,
+                response_dict,
+            )
+            response_dict["outputs"]["tool_delta_message"] = tool_call
         if is_end:
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
+            if req_id in self.tool_parsers:
+                del self.tool_parsers[req_id]
         return response_dict
 
     def process_response_dict(self, response_dict, **kwargs):