Fix Tool Call API & Minor Change

lightllm/common/basemodel/layer_weights/meta_weights/gpt_oss_fused_moe_weight_tp.py

@@ -138,10 +138,10 @@ def experts(self, input_tensor, router_logits, top_k, renormalize, use_grouped_t
             topk_ids=topk_ids,
             inplace=True,
             use_fp8_w8a8=use_fp8_w8a8,
-            w1_scale=w1_scale,
-            w2_scale=w2_scale,
             w1_bias=self.w1_bias,
             w2_bias=self.w2_bias / self.tp_world_size_,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
             layout="interleaved",
             alpha=self.alpha,
             limit=self.limit,

lightllm/common/fused_moe/grouped_fused_moe.py

@@ -764,13 +764,13 @@ def fused_experts_impl(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
     w2: torch.Tensor,
-    w1_bias: Optional[torch.Tensor],
-    w2_bias: Optional[torch.Tensor],
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
     inplace: bool = False,
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
+    w1_bias: Optional[torch.Tensor] = None,
+    w2_bias: Optional[torch.Tensor] = None,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
@@ -890,13 +890,13 @@ def inplace_fused_experts_impl(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
     w2: torch.Tensor,
-    # optional bias for w1 and w2
-    w1_bias: Optional[torch.Tensor],
-    w2_bias: Optional[torch.Tensor],
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
+    # optional bias for w1 and w2
+    w1_bias: Optional[torch.Tensor] = None,
+    w2_bias: Optional[torch.Tensor] = None,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
@@ -909,13 +909,13 @@ def inplace_fused_experts_impl(
         hidden_states,
         w1,
         w2,
-        w1_bias,
-        w2_bias,
         topk_weights,
         topk_ids,
         True,
         use_fp8_w8a8,
         use_int8_w8a16,
+        w1_bias,
+        w2_bias,
         w1_scale,
         w2_scale,
         a1_scale,
@@ -930,13 +930,13 @@ def inplace_fused_experts_impl_fake(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
     w2: torch.Tensor,
-    # optional bias for w1 and w2
-    w1_bias: Optional[torch.Tensor],
-    w2_bias: Optional[torch.Tensor],
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
+    # optional bias for w1 and w2
+    w1_bias: Optional[torch.Tensor] = None,
+    w2_bias: Optional[torch.Tensor] = None,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
@@ -960,13 +960,13 @@ def outplace_fused_experts_impl(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
     w2: torch.Tensor,
-    # optional bias for w1 and w2
-    w1_bias: Optional[torch.Tensor],
-    w2_bias: Optional[torch.Tensor],
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
+    # optional bias for w1 and w2
+    w1_bias: Optional[torch.Tensor] = None,
+    w2_bias: Optional[torch.Tensor] = None,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
@@ -979,13 +979,13 @@ def outplace_fused_experts_impl(
         hidden_states,
         w1,
         w2,
-        w1_bias,
-        w2_bias,
         topk_weights,
         topk_ids,
         False,
         use_fp8_w8a8,
         use_int8_w8a16,
+        w1_bias,
+        w2_bias,
         w1_scale,
         w2_scale,
         a1_scale,
@@ -1051,12 +1051,12 @@ def fused_experts(
             hidden_states,
             w1,
             w2,
-            w1_bias,
-            w2_bias,
             topk_weights,
             topk_ids,
             use_fp8_w8a8,
             use_int8_w8a16,
+            w1_bias,
+            w2_bias,
             w1_scale,
             w2_scale,
             a1_scale,
@@ -1071,12 +1071,12 @@ def fused_experts(
             hidden_states,
             w1,
             w2,
-            w1_bias,
-            w2_bias,
             topk_weights,
             topk_ids,
             use_fp8_w8a8,
             use_int8_w8a16,
+            w1_bias,
+            w2_bias,
             w1_scale,
             w2_scale,
             a1_scale,

lightllm/server/api_models.py

@@ -101,6 +101,7 @@ class ChatCompletionRequest(BaseModel):
     tool_choice: Union[ToolChoice, Literal["auto", "required", "none"]] = Field(
         default="auto", examples=["none"]
     )  # noqa
+    parallel_tool_calls: Optional[bool] = True
 
     # Additional parameters supported by LightLLM
     do_sample: Optional[bool] = False
@@ -122,11 +123,36 @@ class FunctionResponse(BaseModel):
 class ToolCall(BaseModel):
     """Tool call response."""
 
-    id: str
+    id: Optional[str] = None
+    index: Optional[int] = None
     type: Literal["function"] = "function"
     function: FunctionResponse
 
 
+class ChatCompletionMessageGenericParam(BaseModel):
+    role: Literal["system", "assistant", "tool", "function"]
+    content: Union[str, List[MessageContent], None] = Field(default=None)
+    tool_call_id: Optional[str] = None
+    name: Optional[str] = None
+    reasoning_content: Optional[str] = None
+    tool_calls: Optional[List[ToolCall]] = Field(default=None, examples=[None])
+
+    @field_validator("role", mode="before")
+    @classmethod
+    def _normalize_role(cls, v):
+        if isinstance(v, str):
+            v_lower = v.lower()
+            if v_lower not in {"system", "assistant", "tool", "function"}:
+                raise ValueError(
+                    "'role' must be one of 'system', 'assistant', 'tool', or 'function' (case-insensitive)."
+                )
+            return v_lower
+        raise ValueError("'role' must be a string")
+
+
+ChatCompletionMessageParam = Union[ChatCompletionMessageGenericParam, Message]
+
+
 class UsageInfo(BaseModel):
     prompt_tokens: int = 0
     completion_tokens: Optional[int] = 0

lightllm/server/api_openai.py

@@ -9,7 +9,7 @@
 import pickle
 import uuid
 
-from .function_call_parser import TOOLS_TAG_LIST, FunctionCallParser
+from .function_call_parser import TOOLS_TAG_LIST, FunctionCallParser, ToolCallItem
 from .build_prompt import build_prompt, init_tokenizer
 
 asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
@@ -63,6 +63,52 @@ def create_error_response(status_code: HTTPStatus, message: str) -> JSONResponse
     return JSONResponse({"message": message}, status_code=status_code.value)
 
 
+def _process_tool_call_id(
+    tool_call_parser,
+    call_item: ToolCallItem,
+    history_tool_calls_cnt: int,
+) -> str:
+    """Process for generating a new and unique `tool_call_id`"""
+    if tool_call_parser != "kimi_k2":
+        # A simple uuid is sufficient for all models except for Kimi-K2.
+        tool_call_id = f"call_{uuid.uuid4().hex[:24]}"
+        return tool_call_id
+    else:
+        # Align with Kimi-K2 format: functions.{name}:{index}
+        # Kimi-K2 allows multiple tool_calls in one message;
+        # SGLang sets call_item.tool_index to the *local* position inside that message.
+        # Therefore, the index must be corrected by using
+        # `history_tool_calls_cnt + call_item.tool_index` to ensure globally unique and properly ordered.
+        tool_call_id = f"functions.{call_item.name}:{history_tool_calls_cnt+call_item.tool_index}"
+        logger.debug(
+            f"Process tool call idx, parser: {tool_call_parser}, \
+            tool_call_id: {tool_call_id}, \
+            history_cnt: {history_tool_calls_cnt}"
+        )
+        return tool_call_id
+
+
+def _get_history_tool_calls_cnt(request: ChatCompletionRequest) -> int:
+    """Counts the number of tool calls in the request's message history.
+
+    NOTE: This method is only useful for models that include self-increasing
+    history tool call idx in tool calls id, such as kimi-k2
+
+    Args:
+        request: The chat completion request object.
+
+    Returns:
+        The total number of tool calls in the history, or 0 if not applicable.
+    """
+    messages = getattr(request, "messages", [])
+    idx = 0
+    for msg in messages:
+        if msg.role == "assistant":
+            tool_calls = getattr(msg, "tool_calls", None)
+            idx += len(list(tool_calls)) if tool_calls is not None else 0  # noqa
+    return idx
+
+
 async def chat_completions_impl(request: ChatCompletionRequest, raw_request: Request) -> Response:
     from .api_http import g_objs
 
@@ -180,26 +226,31 @@ async def chat_completions_impl(request: ChatCompletionRequest, raw_request: Req
 
             if tool_choice != "none" and any([i in text for i in TOOLS_TAG_LIST]):
                 if finish_reason == "stop":
-                    finish_reason = "function_call"
+                    finish_reason = "tool_calls"
                 try:
                     # 为 tool_call_parser 提供默认值
                     tool_parser = getattr(g_objs.args, "tool_call_parser", None) or "llama3"
                     parser = FunctionCallParser(tools, tool_parser)
                     full_normal_text, call_info_list = parser.parse_non_stream(text)
-                    tool_calls = [
-                        ToolCall(
-                            id=str(call_info.tool_index),
-                            function=FunctionResponse(name=call_info.name, arguments=call_info.parameters),
+                    tool_calls = []
+                    history_tool_calls_cnt = _get_history_tool_calls_cnt(request)
+                    for call_info in call_info_list:
+                        tool_id = _process_tool_call_id(tool_parser, call_info, history_tool_calls_cnt)
+                        tool_calls.append(
+                            ToolCall(
+                                id=tool_id,
+                                index=getattr(call_info, "tool_index", None),
+                                function=FunctionResponse(name=call_info.name, arguments=call_info.parameters),
+                            )
                         )
-                        for call_info in call_info_list
-                    ]
                 except Exception as e:
                     logger.error(f"Exception: {e}")
                     return create_error_response(
                         HTTPStatus.BAD_REQUEST,
                         "Failed to parse fc related info to json format!",
                     )
-
+            if finish_reason == "tool_calls":
+                text = ""
             chat_message = ChatMessage(role="assistant", content=text, tool_calls=tool_calls)
             choice = ChatCompletionResponseChoice(
                 index=i,
@@ -261,6 +312,7 @@ async def stream_results() -> AsyncGenerator[bytes, None]:
                     yield f"data: {chunk.model_dump_json()}\n\n"
 
                 # 2) if we found calls, we output them as separate chunk(s)
+                history_tool_calls_cnt = _get_history_tool_calls_cnt(request)
                 for call_item in calls:
                     # transform call_item -> FunctionResponse + ToolCall
                     if finish_reason == "stop":
@@ -278,17 +330,27 @@ async def stream_results() -> AsyncGenerator[bytes, None]:
                         remaining_call = expected_call.replace(actual_call, "", 1)
                         call_item.parameters = remaining_call
 
+                    if call_item.name:
+                        # First chunk: include ID and function name
+                        tool_call_id = _process_tool_call_id(tool_parser, call_item, history_tool_calls_cnt)
+                        function_name = call_item.name
+                    else:
+                        # Subsequent chunks: null ID and name for argument deltas
+                        tool_call_id = None
+                        function_name = None
+
                     tool_call = ToolCall(
-                        id=str(call_item.tool_index),
+                        id=tool_call_id,
+                        index=getattr(call_item, "tool_index", None),
                         function=FunctionResponse(
-                            name=call_item.name,
+                            name=function_name,
                             arguments=call_item.parameters,
                         ),
                     )
                     choice_data = ChatCompletionStreamResponseChoice(
                         index=0,
                         delta=DeltaMessage(role="assistant", tool_calls=[tool_call]),
-                        finish_reason="function_call",
+                        finish_reason="tool_calls",
                     )
                     chunk = ChatCompletionStreamResponse(
                         id=group_request_id,

lightllm/server/core/objs/start_args_type.py

@@ -30,7 +30,9 @@ class StartArgs:
     mem_fraction: float = field(default=0.9)
     batch_max_tokens: Optional[int] = field(default=None)
     eos_id: List[int] = field(default_factory=list)
-    tool_call_parser: Optional[str] = field(default=None, metadata={"choices": ["llama3", "qwen25", "mistral"]})
+    tool_call_parser: Optional[str] = field(
+        default=None, metadata={"choices": ["llama3", "qwen25", "mistral", "deepseekv3", "kimi_k2", "qwen"]}
+    )
     running_max_req_size: int = field(default=1000)
     tp: int = field(default=1)
     dp: int = field(default=1)