[gpt-oss] Add gpt-oss bf16 support

vllm/entrypoints/openai/tool_parsers/phi4mini_tool_parser.py

@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+from collections.abc import Sequence
+from typing import Any, Optional
+
+import regex as re
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.chat_utils import random_tool_call_id
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaMessage,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("phi4_mini_json")
+class Phi4MiniJsonToolParser(ToolParser):
+    """
+    Tool call parser for phi-4-mini models intended for use with the
+    examples/tool_chat_template_llama.jinja template.
+
+    Used when --enable-auto-tool-choice --tool-call-parser phi4_mini_json  
+    are all set
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase) -> None:
+        super().__init__(tokenizer)
+
+        # initialize properties used for state when parsing tool calls in
+        # streaming mode
+        self.prev_tool_call_arr: list[dict[str, Any]] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: list[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+        self.bot_token: str = "functools"
+
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
+        """
+        Extract the tool calls from a complete model response.
+        """
+        logger.debug("Model output: %s", model_output)
+
+        pattern = r'functools\[(.*?)\]'
+        matches = re.search(pattern, model_output, re.DOTALL)
+
+        if not matches:
+            logger.debug("No function calls found")
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        try:
+            function_call_arr: list[dict[str, Any]] = []
+            try:
+                json_content = '[' + matches.group(1) + ']'
+
+                function_call_arr = json.loads(json_content)
+                logger.debug("Successfully extracted %d function calls",
+                             len(function_call_arr))
+            except json.JSONDecodeError as e:
+                logger.error(
+                    "Failed to parse function calls from model output. "
+                    "Error: %s", str(e))
+
+            tool_calls: list[ToolCall] = [
+                ToolCall(
+                    id=random_tool_call_id(),
+                    type="function",
+                    function=FunctionCall(
+                        name=raw_function_call["name"],
+                        # function call args are JSON but as a string
+                        arguments=json.dumps(
+                            raw_function_call["arguments"]
+                            if "arguments" in raw_function_call else
+                            raw_function_call["parameters"],
+                            ensure_ascii=False),
+                    )) for raw_function_call in function_call_arr
+            ]
+
+            # get any content before the tool call
+            ret = ExtractedToolCallInformation(tools_called=True,
+                                               tool_calls=tool_calls,
+                                               content=None)
+            return ret
+
+        except Exception:
+            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,
+    ) -> Optional[DeltaMessage]:
+
+        return None