[Feature] Support DeepSeekV3 Function Call (#17784)

Signed-off-by: 许文卿 <xwq391974@alibaba-inc.com>
Signed-off-by: Xu Wenqing <xuwq1993@qq.com>

docs/source/features/tool_calling.md

@@ -236,6 +236,13 @@ For Qwen2.5, the chat template in tokenizer_config.json has already included sup
 
 Flags: `--tool-call-parser hermes`
 
+### DeepSeek-V3 Models (`deepseek_v3`)
+
+Supported models:
+* `deepseek-ai/DeepSeek-V3-0324`
+
+Flags: `--tool-call-parser deepseek_v3 --chat-template examples/tool_chat_template_deepseekv3.jinja`
+
 ### Models with Pythonic Tool Calls (`pythonic`)
 
 A growing number of models output a python list to represent tool calls instead of using JSON. This has the advantage of inherently supporting parallel tool calls and removing ambiguity around the JSON schema required for tool calls. The `pythonic` tool parser can support such models.

examples/tool_chat_template_deepseekv3.jinja

+{% if not add_generation_prompt is defined %}
+    {% set add_generation_prompt = false %}
+{% endif %}
+
+{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}
+
+{%- for message in messages %}
+    {%- if message['role'] == 'system' %}
+        {%- if ns.is_first_sp %}
+            {% set ns.system_prompt = ns.system_prompt + message['content'] %}
+            {% set ns.is_first_sp = false %}
+        {%- else %}
+            {% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+
+{{ bos_token }}
+{{ ns.system_prompt }}
+{%- if tools %}
+    {{"\n\n# Tools\n\nYou may call one or more functions to assist with the user query." }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{"\n</tools>\n\n"}}
+
+    {{"For function call returns, you should first print <｜tool▁calls▁begin｜>"}}
+
+    {{"For each function call, you should return object like:\n" }}
+    {{"<｜tool▁call▁begin｜>function<｜tool▁sep｜><function_name>\n```json\n<function_arguments_in_json_format>\n```<｜tool▁call▁end｜>"}}
+
+    {{"At the end of function call returns, you should print <｜tool▁calls▁end｜><｜end▁of▁sentence｜>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if message['role'] == 'user' %}
+        {%- set ns.is_tool = false -%}
+        {%- set ns.is_first = false -%}
+        {%- set ns.is_last_user = true -%}
+        {{'<｜User｜>' + message['content'] + '<｜Assistant｜>'}}
+    {%- endif %}
+
+    {%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}
+        {%- set ns.is_last_user = false -%}
+        {%- if ns.is_tool %}
+            {{'<｜tool▁outputs▁end｜>'}}
+        {%- endif %}
+        {%- set ns.is_first = false %}
+        {%- set ns.is_tool = false -%}
+        {%- set ns.is_output_first = true %}
+        
+        {%- for tool in message['tool_calls'] %}
+            {%- if not ns.is_first %}
+                {%- if message['content'] is none %}
+                    {{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments']|tojson + '\n' + '```' + '<｜tool▁call▁end｜>'}}
+                {%- else %}
+                    {{message['content'] + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments']|tojson + '\n' + '```' + '<｜tool▁call▁end｜>'}}
+                {%- endif %}
+            {%- set ns.is_first = true -%}
+            {%- else %}
+                {{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments']|tojson + '\n' + '```' + '<｜tool▁call▁end｜>'}}
+            {%- endif %}
+        {%- endfor %}
+        {{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}
+    {%- endif %}
+    {%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}
+        {%- set ns.is_last_user = false -%}
+        {%- if ns.is_tool %}
+            {{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}
+            {%- set ns.is_tool = false -%}
+        {%- else %}
+            {% set content = message['content'] %}
+            {{content + '<｜end▁of▁sentence｜>'}}
+        {%- endif %}
+    {%- endif %}
+
+    {%- if message['role'] == 'tool' %}
+        {%- set ns.is_last_user = false -%}
+        {%- set ns.is_tool = true -%}
+        {%- if ns.is_output_first %}
+            {{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}
+            {%- set ns.is_output_first = false %}
+        {%- else %}
+            {{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}
+        {%- endif %}
+    {%- endif %}
+{%- endfor -%}
+
+{% if ns.is_tool %}
+    {{'<｜tool▁outputs▁end｜>'}}
+{% endif %}
+
+{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}
+    {{'<｜Assistant｜>'}}
+{% endif %}

vllm/entrypoints/openai/tool_parsers/__init__.py

 # SPDX-License-Identifier: Apache-2.0
 
 from .abstract_tool_parser import ToolParser, ToolParserManager
+from .deepseekv3_tool_parser import DeepSeekV3ToolParser
 from .granite_20b_fc_tool_parser import Granite20bFCToolParser
 from .granite_tool_parser import GraniteToolParser
 from .hermes_tool_parser import Hermes2ProToolParser
@@ -15,5 +16,5 @@ __all__ = [
     "ToolParser", "ToolParserManager", "Granite20bFCToolParser",
     "GraniteToolParser", "Hermes2ProToolParser", "MistralToolParser",
     "Internlm2ToolParser", "Llama3JsonToolParser", "JambaToolParser",
-    "PythonicToolParser", "Phi4MiniJsonToolParser"
+    "PythonicToolParser", "Phi4MiniJsonToolParser", "DeepSeekV3ToolParser"
 ]

vllm/entrypoints/openai/tool_parsers/deepseekv3_tool_parser.py

+# SPDX-License-Identifier: Apache-2.0
+
+import re
+from collections.abc import Sequence
+from typing import Union
+
+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.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("deepseek_v3")
+class DeepSeekV3ToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = (
+            [])  # map what has been streamed for each tool so far to a list
+
+        self.tool_calls_start_token: str = "<｜tool▁calls▁begin｜>"
+        self.tool_calls_end_token: str = "<｜tool▁calls▁end｜>"
+
+        self.tool_call_start_token: str = "<｜tool▁call▁begin｜>"
+        self.tool_call_end_token: str = "<｜tool▁call▁end｜>"
+
+        self.tool_call_regex = re.compile(
+            r"<｜tool▁call▁begin｜>(?P<type>.*)<｜tool▁sep｜>(?P<function_name>.*)\n```json\n(?P<function_arguments>.*)\n```<｜tool▁call▁end｜>"
+        )
+
+        self.stream_tool_call_portion_regex = re.compile(
+            r"(?P<type>.*)<｜tool▁sep｜>(?P<function_name>.*)\n```json\n(?P<function_arguments>.*[^\n`])"
+        )
+
+        self.stream_tool_call_name_regex = re.compile(
+            r"(?P<type>.*)<｜tool▁sep｜>(?P<function_name>.*)\n")
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction.")
+        self.tool_calls_start_token_id = self.vocab.get(
+            self.tool_calls_start_token)
+        self.tool_calls_end_token_id = self.vocab.get(
+            self.tool_calls_end_token)
+
+        self.tool_call_start_token_id = self.vocab.get(
+            self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+
+        if (self.tool_calls_start_token_id is None
+                or self.tool_calls_end_token_id is None):
+            raise RuntimeError(
+                "DeepSeek-V3 Tool parser could not locate tool call start/end "
+                "tokens in the tokenizer!")
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        # sanity check; avoid unnecessary processing
+        if self.tool_calls_start_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        else:
+            try:
+                # there are two possible captures - between tags, or between a
+                # tag and end-of-string so the result of
+                # findall is an array of tuples where one is a function call and
+                # the other is None
+                function_call_tuples = self.tool_call_regex.findall(
+                    model_output)
+
+                tool_calls = []
+                for match in function_call_tuples:
+                    tool_type, function_name, function_args = match
+                    tool_calls.append(
+                        ToolCall(
+                            type=tool_type,
+                            function=FunctionCall(name=function_name,
+                                                  arguments=function_args),
+                        ))
+
+                content = model_output[:model_output.
+                                       find(self.tool_calls_start_token)]
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None,
+                )
+
+            except Exception:
+                logger.exception(
+                    "Error in extracting tool call from response.")
+                return ExtractedToolCallInformation(tools_called=False,
+                                                    tool_calls=[],
+                                                    content=model_output)
+
+    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]:
+
+        logger.debug("delta_text: %s", delta_text)
+        logger.debug("delta_token_ids: %s", delta_token_ids)
+        # check to see if we should be streaming a tool call - is there a
+        if self.tool_calls_start_token_id not in current_token_ids:
+            logger.debug("No tool call tokens found!")
+            return DeltaMessage(content=delta_text)
+        delta_text = delta_text.replace(self.tool_calls_start_token,
+                                        "").replace(self.tool_calls_end_token,
+                                                    "")
+        try:
+
+            # figure out where we are in the parsing by counting tool call
+            # start & end tags
+            prev_tool_start_count = previous_token_ids.count(
+                self.tool_call_start_token_id)
+            prev_tool_end_count = previous_token_ids.count(
+                self.tool_call_end_token_id)
+            cur_tool_start_count = current_token_ids.count(
+                self.tool_call_start_token_id)
+            cur_tool_end_count = current_token_ids.count(
+                self.tool_call_end_token_id)
+            tool_call_portion = None
+            text_portion = None
+
+            # case: if we're generating text, OR rounding out a tool call
+            if (cur_tool_start_count == cur_tool_end_count
+                    and prev_tool_end_count == cur_tool_end_count
+                    and self.tool_call_end_token not in delta_text):
+                logger.debug("Generating text content! skipping tool parsing.")
+                return DeltaMessage(content=delta_text)
+
+            if self.tool_call_end_token in delta_text:
+                logger.debug("tool_call_end_token in delta_text")
+                full_text = current_text + delta_text
+                tool_call_portion = full_text.split(
+                    self.tool_call_start_token)[-1].split(
+                        self.tool_call_end_token)[0].rstrip()
+                delta_text = delta_text.split(
+                    self.tool_call_end_token)[0].rstrip()
+                text_portion = delta_text.split(
+                    self.tool_call_end_token)[-1].lstrip()
+
+            # case -- we're starting a new tool call
+            if (cur_tool_start_count > cur_tool_end_count
+                    and cur_tool_start_count > prev_tool_start_count):
+                if len(delta_token_ids) > 1:
+                    tool_call_portion = current_text.split(
+                        self.tool_call_start_token)[-1]
+                else:
+                    tool_call_portion = None
+                    delta = None
+
+                text_portion = None
+
+                # set cursors and state appropriately
+                self.current_tool_id += 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("Starting on a new tool %s", self.current_tool_id)
+
+            # case -- we're updating an existing tool call
+            elif (cur_tool_start_count > cur_tool_end_count
+                  and cur_tool_start_count == prev_tool_start_count):
+
+                # get the portion of the text that's the tool call
+                tool_call_portion = current_text.split(
+                    self.tool_call_start_token)[-1]
+                text_portion = None
+
+            # case -- the current tool call is being closed.
+            elif (cur_tool_start_count == cur_tool_end_count
+                  and cur_tool_end_count >= prev_tool_end_count):
+                if self.prev_tool_call_arr is None or len(
+                        self.prev_tool_call_arr) == 0:
+                    logger.debug(
+                        "attempting to close tool call, but no tool call")
+                    return None
+                diff = self.prev_tool_call_arr[self.current_tool_id].get(
+                    "arguments")
+                if diff:
+                    diff = (diff.encode("utf-8").decode("unicode_escape")
+                            if diff is str else diff)
+                    if '"}' not in delta_text:
+                        return None
+                    end_loc = delta_text.rindex('"}')
+                    diff = delta_text[:end_loc] + '"}'
+                    logger.debug(
+                        "Finishing tool and found diff that had not "
+                        "been streamed yet: %s",
+                        diff,
+                    )
+                    self.streamed_args_for_tool[self.current_tool_id] += diff
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(
+                            index=self.current_tool_id,
+                            function=DeltaFunctionCall(
+                                arguments=diff).model_dump(exclude_none=True),
+                        )
+                    ])
+
+            # case -- otherwise we're just generating text
+            else:
+                text = delta_text.replace(self.tool_call_start_token, "")
+                text = text.replace(self.tool_call_end_token, "")
+                delta = DeltaMessage(tool_calls=[], content=text)
+                return delta
+
+            current_tool_call = dict()
+            if tool_call_portion:
+                current_tool_call_matches = (
+                    self.stream_tool_call_portion_regex.match(
+                        tool_call_portion))
+                if current_tool_call_matches:
+                    tool_type, tool_name, tool_args = (
+                        current_tool_call_matches.groups())
+                    current_tool_call["name"] = tool_name
+                    current_tool_call["arguments"] = tool_args
+                else:
+                    current_tool_call_name_matches = (
+                        self.stream_tool_call_name_regex.match(
+                            tool_call_portion))
+                    if current_tool_call_name_matches:
+                        tool_type, tool_name = (
+                            current_tool_call_name_matches.groups())
+                        current_tool_call["name"] = tool_name
+                        current_tool_call["arguments"] = ""
+                    else:
+                        logger.debug("Not enough token")
+                        return None
+
+            # case - we haven't sent the tool name yet. If it's available, send
+            #   it. otherwise, wait until it's available.
+            if not self.current_tool_name_sent:
+                if current_tool_call is None:
+                    return None
+                function_name: Union[str, None] = current_tool_call.get("name")
+                if function_name:
+                    self.current_tool_name_sent = True
+                    return 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),
+                        )
+                    ])
+                else:
+                    return None
+
+            # case -- otherwise, send the tool call delta
+
+            # if the tool call portion is None, send the delta as text
+            if tool_call_portion is None:
+                # if there's text but not tool calls, send that -
+                # otherwise None to skip chunk
+                delta = (DeltaMessage(
+                    content=delta_text) if text_portion is not None else None)
+                return delta
+
+            # now, the nitty-gritty of tool calls
+            # now we have the portion to parse as tool call.
+
+            logger.debug("Trying to parse current tool call with ID %s",
+                         self.current_tool_id)
+
+            # if we're starting a new tool call, push an empty object in as
+            #   a placeholder for the arguments
+            if len(self.prev_tool_call_arr) <= self.current_tool_id:
+                self.prev_tool_call_arr.append({})
+
+            # main logic for tool parsing here - compare prev. partially-parsed
+            #   JSON to the current partially-parsed JSON
+            prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
+                "arguments")
+            cur_arguments = current_tool_call.get("arguments")
+
+            logger.debug("diffing old arguments: %s", prev_arguments)
+            logger.debug("against new ones: %s", cur_arguments)
+
+            # case -- no arguments have been created yet. skip sending a delta.
+            if not cur_arguments and not prev_arguments:
+                logger.debug("Skipping text %s - no arguments", delta_text)
+                delta = None
+
+            # case -- prev arguments are defined, but non are now.
+            #   probably impossible, but not a fatal error - just keep going
+            elif not cur_arguments and prev_arguments:
+                logger.error("should be impossible to have arguments reset "
+                             "mid-call. skipping streaming anything.")
+                delta = None
+
+            # case -- we now have the first info about arguments available from
+            #   autocompleting the JSON
+            elif cur_arguments and not prev_arguments:
+
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(
+                        index=self.current_tool_id,
+                        function=DeltaFunctionCall(
+                            arguments=cur_arguments).model_dump(
+                                exclude_none=True),
+                    )
+                ])
+                self.streamed_args_for_tool[
+                    self.current_tool_id] = cur_arguments
+
+            # last case -- we have an update to existing arguments.
+            elif cur_arguments and prev_arguments:
+                if (isinstance(delta_text, str)
+                        and cur_arguments != prev_arguments
+                        and len(cur_arguments) > len(prev_arguments)
+                        and cur_arguments.startswith(prev_arguments)):
+                    delta_arguments = cur_arguments[len(prev_arguments):]
+                    logger.debug("got diff %s", delta_text)
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(
+                            index=self.current_tool_id,
+                            function=DeltaFunctionCall(
+                                arguments=delta_arguments).model_dump(
+                                    exclude_none=True),
+                        )
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] = cur_arguments
+                else:
+                    delta = None
+
+            # handle saving the state for the current tool into
+            # the "prev" list for use in diffing for the next iteration
+            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
+                self.prev_tool_call_arr[
+                    self.current_tool_id] = current_tool_call
+            else:
+                self.prev_tool_call_arr.append(current_tool_call)
+
+            return delta
+
+        except Exception:
+            logger.exception("Error trying to handle streaming tool call.")
+            return None  # do not stream a delta. skip this token ID.