[Frontend][Feature] support tool calling for internlm/internlm2_5-7b-…

…chat model (vllm-project#8405)

docs/requirements-docs.txt

@@ -12,4 +12,5 @@ torch
 py-cpuinfo
 transformers
 mistral_common >= 1.3.4
-openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args
+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 @@ def ensure_system_prompt(messages: List[Dict[str, Any]],
         "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 @@ def ensure_system_prompt(messages: List[Dict[str, Any]],
                     "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_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 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 @@
                                                     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 @@ def __init__(self,
 
         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 @@ async def create_chat_completion(
             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 @@ async def chat_completion_stream_generator(
         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 @@ async def chat_completion_stream_generator(
                     # 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 @@ async def chat_completion_stream_generator(
 
                 for output in res.outputs:
                     i = output.index
+                    tool_parser = tool_parsers[i]
 
                     if finish_reason_sent[i]:
                         continue
@@ -446,7 +446,8 @@ async def chat_completion_stream_generator(
                                 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 @@ async def chat_completion_full_generator(
                     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"
 ]