[Frontend][Feature] support tool calling for internlm/internlm2_5-7b-chat model (#8405)

docs/requirements-docs.txt

@@ -13,3 +13,4 @@ py-cpuinfo
 transformers
 mistral_common >= 1.3.4
 openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args
+partial-json-parser # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args

docs/source/serving/openai_compatible_server.md

@@ -157,8 +157,9 @@ vLLM will use guided decoding to ensure the response matches the tool parameter
 To enable this feature, you should set the following flags:
 * `--enable-auto-tool-choice` -- **mandatory** Auto tool choice. tells vLLM that you want to enable the model to generate its own tool calls when it 
 deems appropriate.
-* `--tool-call-parser` -- select the tool parser to use - currently either `hermes`, `mistral` or `llama3_json`. Additional tool parsers 
-will continue to be added in the future.
+* `--tool-call-parser` -- select the tool parser to use - currently either `hermes` or `mistral` or `llama3_json` or `internlm`. Additional tool parsers 
+will continue to be added in the future, and also can register your own tool parsers in the `--tool-parser-plugin`.
+* `--tool-parser-plugin` -- **optional** tool parser plugin used to register user defined tool parsers into vllm, the registered tool parser name can be specified in `--tool-call-parser`.
 * `--chat-template` -- **optional** for auto tool choice. the path to the chat template which handles `tool`-role messages and `assistant`-role messages 
 that contain previously generated tool calls. Hermes, Mistral and Llama models have tool-compatible chat templates in their 
 `tokenizer_config.json` files, but you can specify a custom template. This argument can be set to `tool_use` if your model has a tool use-specific chat 
@@ -218,4 +219,73 @@ it works better with vLLM.
 
 Recommended flags: `--tool-call-parser llama3_json --chat-template examples/tool_chat_template_llama3_json.jinja`
 
+#### Internlm Models
+Supported models:
+* `internlm/internlm2_5-7b-chat` (confirmed)
+* Additional internlm2.5 function-calling models are compatible as well
+
+Known issues:
+* Although this implementation also supports Internlm2, the tool call results are not stable when testing with the `internlm/internlm2-chat-7b` model.
+
+Recommended flags: `--tool-call-parser internlm --chat-template examples/tool_chat_template_internlm2_tool.jinja`
+
+
+### How to write a tool parser plugin
+
+A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py.
+
+Here is a summary of a plugin file:
+
+```python
+
+# import the required packages
+
+# define a tool parser and register it to vllm
+# the name list in register_module can be used
+# in --tool-call-parser. you can define as many
+# tool parsers as you want here.
+@ToolParserManager.register_module(["example"])
+class ExampleToolParser(ToolParser):
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+    # adjust request. e.g.: set skip special tokens
+    # to False for tool call output.
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        return request
+
+    # implement the tool call parse for stream call
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+        return delta
+
+    # implement the tool parse for non-stream call
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        return ExtractedToolCallInformation(tools_called=False,
+                                            tool_calls=[],
+                                            content=text)
+
+
+```
+
+Then you can use this plugin in the command line like this.
+```
+    --enable-auto-tool-choice \
+    --tool-parser-plugin <absolute path of the plugin file>
+    --tool-call-parser example \
+    --chat-template <your chat template> \
+``` 
 

examples/tool_chat_template_internlm2_tool.jinja

@@ -0,0 +1,60 @@
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{{- bos_token }}
+{%- if system_message is defined %}
+{{- "<|im_start|>system\n" + system_message + "<|im_end|>\n" }}
+{%- endif %}
+
+{%- if tools is not none %}
+    {{- "<|im_start|>system name=<|plugin|>\n[" }}
+    {%- for tool in tools %}
+        {{- tool.function|tojson }}
+        {%- if not loop.last %}
+            {{- ", " }}
+        {%- else %}
+            {{- "]" }}
+        {%- endif %}
+    {%- endfor %}
+    {{- "<|im_end|>\n" }}
+{%- endif %}
+
+{%- for message in loop_messages %}
+    {%- if message["role"] == "user" %}
+        {{- "<|im_start|>user\n" + message["content"] + "<|im_end|>\n"}}
+    {%- elif message.tool_calls is defined and message.tool_calls is not none %}
+        {%- set content = message["content"] if message["content"] else "" %}
+        {{- "<|im_start|>assistant\n" + content }}
+        {%- for tool_call in message.tool_calls %}
+            {%- set function=tool_call.function %}
+            {{- "<|action_start|><|plugin|>\n" }}
+            {{- '{"name": "' + function.name + '", '}}
+            {{- '"arguments": ' + function.arguments|tojson + '}' }}
+            {{- "<|action_end|>" }}
+        {%- endfor %}
+        {{- "<|im_end|>\n" }}
+    {%- elif message["role"] == "assistant" %}
+        {{- "<|im_start|>assistant\n" + message["content"] + "<|im_end|>\n"}}
+    {%- elif message["role"] == "tool_results" or message["role"] == "tool" or message["role"] == "function" %}
+        {%- if message.content is defined and message.content.content is defined %}
+            {%- set content = message.content.content %}
+        {%- else %}
+            {%- set content = message.content %}
+        {%- endif %}
+        {{- "<|im_start|>environment name=<|plugin|>\n" + content|string + "<|im_end|>\n" }}
+    {%- else %}
+        {{- raise_exception("Only user and assistant and tool_results and tool and function roles are supported, with the exception of an initial optional system message!") }}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+{{- '<|im_start|>assistant\n' }}
+{%- endif %}

tests/tool_use/utils.py

@@ -87,6 +87,18 @@ CONFIGS: Dict[str, ServerConfig] = {
         "call the tool. Otherwise, answer the user's query directly "
         "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
         "to the user's question - just respond to it normally."
+    },
+    "internlm": {
+        "model":
+        "internlm/internlm2_5-7b-chat",
+        "arguments": [
+            "--tool-call-parser", "internlm", "--chat-template",
+            str(VLLM_PATH /
+                "examples/tool_chat_template_internlm2_tool.jinja"),
+            "--trust_remote_code"
+        ],
+        "supports_parallel":
+        False,
     }
 }
 
@@ -109,7 +121,7 @@ WEATHER_TOOL: ChatCompletionToolParam = {
                     "type":
                     "string",
                     "description":
-                    "the two-letter abbreviation for the state "
+                    "must the two-letter abbreviation for the state "
                     "that the city is in, e.g. 'CA' which would "
                     "mean 'California'"
                 },

vllm/entrypoints/openai/api_server.py

@@ -53,6 +53,7 @@ from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
 from vllm.entrypoints.openai.serving_engine import BaseModelPath
 from vllm.entrypoints.openai.serving_tokenization import (
     OpenAIServingTokenization)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.logger import init_logger
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import FlexibleArgumentParser, get_open_zmq_ipc_path
@@ -526,6 +527,15 @@ async def run_server(args, **uvicorn_kwargs) -> None:
     logger.info("vLLM API server version %s", VLLM_VERSION)
     logger.info("args: %s", args)
 
+    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
+        ToolParserManager.import_tool_parser(args.tool_parser_plugin)
+
+    valide_tool_parses = ToolParserManager.tool_parsers.keys()
+    if args.enable_auto_tool_choice \
+        and args.tool_call_parser not in valide_tool_parses:
+        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
+                       f"(chose from {{ {','.join(valide_tool_parses)} }})")
+
     temp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     temp_socket.bind(("", args.port))
 

vllm/entrypoints/openai/cli_args.py

@@ -12,6 +12,7 @@ from typing import List, Optional, Sequence, Union
 from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
 from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
                                                     PromptAdapterPath)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.utils import FlexibleArgumentParser
 
 
@@ -190,16 +191,27 @@ def make_arg_parser(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         "Enable auto tool choice for supported models. Use --tool-call-parser"
         "to specify which parser to use")
 
+    valid_tool_parsers = ToolParserManager.tool_parsers.keys()
     parser.add_argument(
         "--tool-call-parser",
         type=str,
-        choices=["mistral", "hermes", "llama3_json"],
+        metavar="{" + ",".join(valid_tool_parsers) + "} or name registered in "
+        "--tool-parser-plugin",
         default=None,
         help=
         "Select the tool call parser depending on the model that you're using."
         " This is used to parse the model-generated tool call into OpenAI API "
         "format. Required for --enable-auto-tool-choice.")
 
+    parser.add_argument(
+        "--tool-parser-plugin",
+        type=str,
+        default="",
+        help=
+        "Special the tool parser plugin write to parse the model-generated tool"
+        " into OpenAI API format, the name register in this plugin can be used "
+        "in --tool-call-parser.")
+
     parser = AsyncEngineArgs.add_cli_args(parser)
 
     parser.add_argument('--max-log-len',

vllm/entrypoints/openai/serving_chat.py

@@ -29,10 +29,7 @@ from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
                                                     OpenAIServing,
                                                     PromptAdapterPath,
                                                     TextTokensPrompt)
-from vllm.entrypoints.openai.tool_parsers import (Hermes2ProToolParser,
-                                                  Llama3JsonToolParser,
-                                                  MistralToolParser,
-                                                  ToolParser)
+from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
 from vllm.inputs import TokensPrompt
 from vllm.logger import init_logger
 from vllm.outputs import CompletionOutput, RequestOutput
@@ -82,15 +79,13 @@ class OpenAIServingChat(OpenAIServing):
 
         self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
         if self.enable_auto_tools:
-            if tool_parser == "mistral":
-                self.tool_parser = MistralToolParser
-            elif tool_parser == "hermes":
-                self.tool_parser = Hermes2ProToolParser
-            elif tool_parser == "llama3_json":
-                self.tool_parser = Llama3JsonToolParser
-            else:
+            try:
+                self.tool_parser = ToolParserManager.get_tool_parser(
+                    tool_parser)
+            except Exception as e:
                 raise TypeError("Error: --enable-auto-tool-choice requires "
-                                "--tool-call-parser")
+                                f"tool_parser:'{tool_parser}' which has not "
+                                "been registered") from e
 
     async def create_chat_completion(
         self,
@@ -187,6 +182,10 @@ class OpenAIServingChat(OpenAIServing):
             raw_request.state.request_metadata = request_metadata
 
         try:
+            if self.enable_auto_tools and self.tool_parser:
+                request = self.tool_parser(tokenizer).adjust_request(
+                    request=request)
+
             if isinstance(prompt, str):
                 prompt_inputs = self._tokenize_prompt_input(
                     request,
@@ -282,11 +281,11 @@ class OpenAIServingChat(OpenAIServing):
         num_choices = 1 if request.n is None else request.n
         previous_num_tokens = [0] * num_choices
         finish_reason_sent = [False] * num_choices
-
         num_prompt_tokens = 0
 
-        tool_parser: Optional[ToolParser] = self.tool_parser(
-            tokenizer) if self.tool_parser else None
+        tool_parsers: List[Optional[ToolParser]] = [
+            self.tool_parser(tokenizer) if self.tool_parser else None
+        ] * num_choices
 
         if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
             tool_choice_function_name = request.tool_choice.function.name
@@ -324,7 +323,7 @@ class OpenAIServingChat(OpenAIServing):
                     # NOTE num_choices defaults to 1 so this usually executes
                     # once per request
                     for i in range(num_choices):
-
+                        tool_parser = tool_parsers[i]
                         choice_data = ChatCompletionResponseStreamChoice(
                             index=i,
                             delta=DeltaMessage(
@@ -399,6 +398,7 @@ class OpenAIServingChat(OpenAIServing):
 
                 for output in res.outputs:
                     i = output.index
+                    tool_parser = tool_parsers[i]
 
                     if finish_reason_sent[i]:
                         continue
@@ -446,7 +446,8 @@ class OpenAIServingChat(OpenAIServing):
                                 delta_text=delta_text,
                                 previous_token_ids=previous_token_ids,
                                 current_token_ids=current_token_ids,
-                                delta_token_ids=output.token_ids))
+                                delta_token_ids=output.token_ids,
+                                request=request))
 
                         # update the previous values for the next iteration
                         previous_texts[i] = current_text
@@ -685,7 +686,8 @@ class OpenAIServingChat(OpenAIServing):
                     and self.tool_parser:
 
                 tool_parser = self.tool_parser(tokenizer)
-                tool_call_info = tool_parser.extract_tool_calls(output.text)
+                tool_call_info = tool_parser.extract_tool_calls(
+                    output.text, request=request)
                 tools_called = tool_call_info.tools_called
                 if tool_call_info.tools_called:
                     message = ChatMessage(role=role,

vllm/entrypoints/openai/tool_parsers/__init__.py

@@ -1,9 +1,10 @@
-from .abstract_tool_parser import ToolParser
+from .abstract_tool_parser import ToolParser, ToolParserManager
 from .hermes_tool_parser import Hermes2ProToolParser
+from .internlm2_tool_parser import Internlm2ToolParser
 from .llama_tool_parser import Llama3JsonToolParser
 from .mistral_tool_parser import MistralToolParser
 
 __all__ = [
-    "ToolParser", "Hermes2ProToolParser", "MistralToolParser",
-    "Llama3JsonToolParser"
+    "ToolParser", "ToolParserManager", "Hermes2ProToolParser",
+    "MistralToolParser", "Internlm2ToolParser", "Llama3JsonToolParser"
 ]

vllm/entrypoints/openai/tool_parsers/abstract_tool_parser.py

@@ -1,9 +1,14 @@
-from typing import Dict, List, Sequence, Union
+import importlib
+import importlib.util
+import os
+from typing import Callable, Dict, List, Optional, Sequence, Type, Union
 
-from vllm.entrypoints.openai.protocol import (DeltaMessage,
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaMessage,
                                               ExtractedToolCallInformation)
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import is_list_of
 
 logger = init_logger(__name__)
 
@@ -24,8 +29,16 @@ class ToolParser:
 
         self.model_tokenizer = tokenizer
 
-    def extract_tool_calls(self,
-                           model_output: str) -> ExtractedToolCallInformation:
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        """
+        Static method that used to adjust the request parameters.
+        """
+        return request
+
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
         """
         Static method that should be implemented for extracting tool calls from
         a complete model-generated string.
@@ -44,6 +57,7 @@ class ToolParser:
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
     ) -> Union[DeltaMessage, None]:
         """
         Instance method that should be implemented for extracting tool calls
@@ -55,3 +69,86 @@ class ToolParser:
         raise NotImplementedError(
             "AbstractToolParser.extract_tool_calls_streaming has not been "
             "implemented!")
+
+
+class ToolParserManager:
+    tool_parsers: Dict[str, Type] = {}
+
+    @classmethod
+    def get_tool_parser(cls, name) -> Type:
+        """
+        Get tool parser by name which is registered by `register_module`.
+
+        Raise a KeyError exception if the name is not registered.
+        """
+        if name in cls.tool_parsers:
+            return cls.tool_parsers[name]
+
+        raise KeyError(f"tool helper: '{name}' not found in tool_parsers")
+
+    @classmethod
+    def _register_module(cls,
+                         module: Type,
+                         module_name: Optional[Union[str, List[str]]] = None,
+                         force: bool = True) -> None:
+        if not issubclass(module, ToolParser):
+            raise TypeError(
+                f'module must be subclass of ToolParser, but got {type(module)}'
+            )
+        if module_name is None:
+            module_name = module.__name__
+        if isinstance(module_name, str):
+            module_name = [module_name]
+        for name in module_name:
+            if not force and name in cls.tool_parsers:
+                existed_module = cls.tool_parsers[name]
+                raise KeyError(f'{name} is already registered '
+                               f'at {existed_module.__module__}')
+            cls.tool_parsers[name] = module
+
+    @classmethod
+    def register_module(
+            cls,
+            name: Optional[Union[str, List[str]]] = None,
+            force: bool = True,
+            module: Union[Type, None] = None) -> Union[type, Callable]:
+        """
+        Register module with the given name or name list. it can be used as a
+        decoder(with module as None) or normal function(with module as not 
+        None).
+        """
+        if not isinstance(force, bool):
+            raise TypeError(f'force must be a boolean, but got {type(force)}')
+
+        # raise the error ahead of time
+        if not (name is None or isinstance(name, str)
+                or is_list_of(name, str)):
+            raise TypeError(
+                'name must be None, an instance of str, or a sequence of str, '
+                f'but got {type(name)}')
+
+        # use it as a normal method: x.register_module(module=SomeClass)
+        if module is not None:
+            cls._register_module(module=module, module_name=name, force=force)
+            return module
+
+        # use it as a decorator: @x.register_module()
+        def _register(module):
+            cls._register_module(module=module, module_name=name, force=force)
+            return module
+
+        return _register
+
+    @classmethod
+    def import_tool_parser(cls, plugin_path: str) -> None:
+        """
+        Import a user defined tool parser by the path of the tool parser define
+        file.
+        """
+        module_name = os.path.splitext(os.path.basename(plugin_path))[0]
+        spec = importlib.util.spec_from_file_location(module_name, plugin_path)
+        if spec is None or spec.loader is None:
+            logger.error("load %s from %s failed.", module_name, plugin_path)
+            return
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)

vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py

@@ -5,12 +5,13 @@ from typing import Dict, List, Sequence, Union
 import partial_json_parser
 from partial_json_parser.core.options import Allow
 
-from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
                                               ExtractedToolCallInformation,
                                               FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
-    ToolParser)
+    ToolParser, ToolParserManager)
 from vllm.entrypoints.openai.tool_parsers.utils import (
     extract_intermediate_diff)
 from vllm.logger import init_logger
@@ -20,6 +21,7 @@ from vllm.utils import random_uuid
 logger = init_logger(__name__)
 
 
+@ToolParserManager.register_module("hermes")
 class Hermes2ProToolParser(ToolParser):
 
     def __init__(self, tokenizer: AnyTokenizer):
@@ -57,8 +59,11 @@ class Hermes2ProToolParser(ToolParser):
                 "Hermes 2 Pro Tool parser could not locate tool call start/end "
                 "tokens in the tokenizer!")
 
-    def extract_tool_calls(self,
-                           model_output: str) -> ExtractedToolCallInformation:
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
 
         # sanity check; avoid unnecessary processing
         if self.tool_call_start_token not in model_output:
@@ -114,6 +119,7 @@ class Hermes2ProToolParser(ToolParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
     ) -> Union[DeltaMessage, None]:
 
         logger.debug("delta_text: %s", delta_text)

vllm/entrypoints/openai/tool_parsers/internlm2_tool_parser.py

@@ -0,0 +1,208 @@
+import json
+from typing import Dict, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module(["internlm"])
+class Internlm2ToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+        self.position = 0
+
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        if request.tools and request.tool_choice != 'none':
+            # do not skip special tokens because internlm use the special
+            # tokens to indicated the start and end of the tool calls
+            # information.
+            request.skip_special_tokens = False
+        return request
+
+    def get_argments(self, obj):
+        if "parameters" in obj:
+            return obj.get("parameters")
+        elif "arguments" in obj:
+            return obj.get("arguments")
+        return None
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+        if '<|action_start|>' not in current_text:
+            self.position = len(current_text)
+            return DeltaMessage(content=delta_text)
+        # if the tool call is sended, return a empty delta message
+        # to make sure the finish_reason will be send correctly.
+        if self.current_tool_id > 0:
+            return DeltaMessage(content='')
+
+        last_pos = self.position
+        if '<|action_start|><|plugin|>' not in current_text[last_pos:]:
+            return None
+
+        new_delta = current_text[last_pos:]
+        text, action = new_delta.split('<|action_start|><|plugin|>')
+
+        if len(text) > 0:
+            self.position = self.position + len(text)
+            return DeltaMessage(content=text)
+
+        action = action.strip()
+        action = action.split('<|action_end|>'.strip())[0]
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+
+        try:
+            parsable_arr = action
+
+            # tool calls are generated in an object in inernlm2
+            # it's not support parallel tool calls
+            try:
+                tool_call_arr: Dict = partial_json_parser.loads(
+                    parsable_arr, flags)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            if not self.current_tool_name_sent:
+                function_name = tool_call_arr.get("name")
+                if function_name:
+                    self.current_tool_id = self.current_tool_id + 1
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                    self.streamed_args_for_tool.append("")
+                else:
+                    delta = None
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+                prev_arguments = self.get_argments(
+                    self.prev_tool_call_arr[self.current_tool_id])
+                cur_arguments = self.get_argments(tool_call_arr)
+
+                # not arguments generated
+                if not cur_arguments and not prev_arguments:
+                    delta = None
+                # will never happen
+                elif not cur_arguments and prev_arguments:
+                    logger.error(
+                        "INVARIANT - impossible to have arguments reset "
+                        "mid-arguments")
+                    delta = None
+                # first time to get parameters
+                elif cur_arguments and not prev_arguments:
+                    cur_arguments_json = json.dumps(cur_arguments)
+
+                    arguments_delta = cur_arguments_json[:cur_arguments_json.
+                                                         index(delta_text) +
+                                                         len(delta_text)]
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=arguments_delta).
+                                      model_dump(exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += arguments_delta
+                # both prev and cur parameters, send the increase parameters
+                elif cur_arguments and prev_arguments:
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_args_json = json.dumps(prev_arguments)
+
+                    argument_diff = extract_intermediate_diff(
+                        cur_args_json, prev_args_json)
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=argument_diff).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += argument_diff
+
+            # check to see if the name is defined and has been sent. if so,
+            # stream the name - otherwise keep waiting
+            # finish by setting old and returning None as base case
+            tool_call_arr["arguments"] = self.get_argments(tool_call_arr)
+            self.prev_tool_call_arr = [tool_call_arr]
+            return delta
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        text = model_output
+        tools = request.tools
+        if '<|action_start|><|plugin|>' in text:
+            text, action = text.split('<|action_start|><|plugin|>')
+            action = action.split('<|action_end|>'.strip())[0]
+            action = action[action.find('{'):]
+            action_dict = json.loads(action)
+            name, parameters = action_dict['name'], json.dumps(
+                action_dict.get('parameters', action_dict.get('arguments',
+                                                              {})))
+
+            if not tools or name not in [t.function.name for t in tools]:
+                ExtractedToolCallInformation(tools_called=False,
+                                             tool_calls=[],
+                                             content=text)
+
+            tool_calls = [
+                ToolCall(
+                    function=FunctionCall(name=name, arguments=parameters))
+            ]
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=text if len(text) > 0 else None)
+
+        return ExtractedToolCallInformation(tools_called=False,
+                                            tool_calls=[],
+                                            content=text)

vllm/entrypoints/openai/tool_parsers/llama_tool_parser.py

@@ -7,12 +7,13 @@ import partial_json_parser
 from partial_json_parser.core.options import Allow
 from transformers import PreTrainedTokenizerBase
 
-from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
                                               ExtractedToolCallInformation,
                                               FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
-    ToolParser)
+    ToolParser, ToolParserManager)
 from vllm.entrypoints.openai.tool_parsers.utils import find_common_prefix
 from vllm.logger import init_logger
 from vllm.utils import random_uuid
@@ -41,6 +42,7 @@ def is_complete_json(input_str):
         return False
 
 
+@ToolParserManager.register_module("llama3_json")
 class Llama3JsonToolParser(ToolParser):
     """
     Tool call parser for Llama 3.1 models intended for use with the
@@ -64,8 +66,9 @@ class Llama3JsonToolParser(ToolParser):
                                              add_special_tokens=False)[0]
         self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
 
-    def extract_tool_calls(self,
-                           model_output: str) -> ExtractedToolCallInformation:
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
         """
         Extract the tool calls from a complete model response.
         """
@@ -125,6 +128,7 @@ class Llama3JsonToolParser(ToolParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
     ) -> Union[DeltaMessage, None]:
 
         if not (current_text.startswith(self.bot_token)

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -8,12 +8,13 @@ import partial_json_parser
 from partial_json_parser.core.options import Allow
 from pydantic import Field
 
-from vllm.entrypoints.openai.protocol import (DeltaFunctionCall, DeltaMessage,
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
                                               DeltaToolCall,
                                               ExtractedToolCallInformation,
                                               FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
-    ToolParser)
+    ToolParser, ToolParserManager)
 from vllm.entrypoints.openai.tool_parsers.utils import (
     extract_intermediate_diff)
 from vllm.logger import init_logger
@@ -36,6 +37,7 @@ class MistralToolCall(ToolCall):
         return "".join(choices(ALPHANUMERIC, k=9))
 
 
+@ToolParserManager.register_module("mistral")
 class MistralToolParser(ToolParser):
     """
     Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
@@ -47,9 +49,7 @@ class MistralToolParser(ToolParser):
     def __init__(self, tokenizer: AnyTokenizer):
         super().__init__(tokenizer)
 
-        if isinstance(self.model_tokenizer, MistralTokenizer):
-            self.model_tokenizer = self.model_tokenizer.tokenizer
-        else:
+        if not isinstance(self.model_tokenizer, MistralTokenizer):
             logger.info("Non-Mistral tokenizer detected when using a Mistral "
                         "model...")
 
@@ -61,11 +61,14 @@ class MistralToolParser(ToolParser):
         self.streamed_args_for_tool: List[str] = [
         ]  # map what has been streamed for each tool so far to a list
         self.bot_token = "[TOOL_CALLS]"
-        self.bot_token_id = self.model_tokenizer.vocab[self.bot_token]
+        self.bot_token_id = self.model_tokenizer.get_vocab()[self.bot_token]
         self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
 
-    def extract_tool_calls(self,
-                           model_output: str) -> ExtractedToolCallInformation:
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
         """
         Extract the tool calls from a complete model response. Requires
         find-and-replacing single quotes with double quotes for JSON parsing,
@@ -119,6 +122,7 @@ class MistralToolParser(ToolParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
     ) -> Union[DeltaMessage, None]:
 
         # if the tool call token is not in the tokens generated so far, append