Feature/intern s1 parser

docs/features/reasoning_outputs.md

@@ -21,6 +21,7 @@ vLLM currently supports the following reasoning models:
 | [Holo2 series](https://huggingface.co/collections/Hcompany/holo2) | `holo2` | `json`, `regex` | ✅ |
 | [Hunyuan A13B series](https://huggingface.co/collections/tencent/hunyuan-a13b-685ec38e5b46321e3ea7c4be) | `hunyuan_a13b` | `json`, `regex` | ✅ |
 | [IBM Granite 3.2 language models](https://huggingface.co/collections/ibm-granite/granite-32-language-models-67b3bc8c13508f6d064cff9a) | `granite` | ❌ | ❌ |
+| [Intern-S1](https://huggingface.co/internlm/Intern-S1) / [Intern-S1-mini](https://huggingface.co/internlm/Intern-S1-mini) | `intern-s1` | ❌ | ✅ |
 | [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) | `minimax_m2_append_think` | `json`, `regex` | ✅ |
 | [Qwen3 series](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) | `qwen3` | `json`, `regex` | ✅ |
 | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | `deepseek_r1` | `json`, `regex` | ✅ |
@@ -75,6 +76,15 @@ Next, make a request to the model that should return the reasoning content in th
 
 The `reasoning` field contains the reasoning steps that led to the final conclusion, while the `content` field contains the final conclusion.
 
+For Intern-S1 models that use both reasoning and tool calling, start the
+server with both parsers enabled:
+
+```bash
+vllm serve internlm/Intern-S1 \
+    --tool-call-parser intern-s1 \
+    --reasoning-parser intern-s1
+```
+
 ## Streaming chat completions
 
 Streaming chat completions are also supported for reasoning models. The `reasoning` field is available in the `delta` field in [chat completion response chunks](https://platform.openai.com/docs/api-reference/chat/streaming).

docs/features/tool_calling.md

@@ -276,6 +276,25 @@ Known issues:
 
 Recommended flags: `--tool-call-parser internlm --chat-template examples/tool_chat_template_internlm2_tool.jinja`
 
+### Intern-S1 Models (`intern-s1`)
+
+Supported models:
+
+* `internlm/Intern-S1`
+* `internlm/Intern-S1-mini`
+
+Recommended flags: `--tool-call-parser intern-s1 --reasoning-parser intern-s1`
+
+Notes:
+
+* Intern-S1 tool calls can appear inside `<think>...</think>`, so the
+  dedicated `intern-s1` reasoning parser should be used together with the
+  `intern-s1` tool parser for non-streaming tool calling.
+* Use the model's default chat template instead of
+  `examples/tool_chat_template_internlm2_tool.jinja`.
+* This keeps Intern-S1 handling separate from the existing `internlm` parser
+  and avoids changing tool-calling behavior for other InternLM-family models.
+
 ### Jamba Models (`jamba`)
 
 AI21's Jamba-1.5 models are supported.

tests/reasoning/test_intern_s1_reasoning_parser.py

@@ -0,0 +1,209 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+    ChatCompletionToolsParam,
+)
+from vllm.entrypoints.openai.engine.protocol import FunctionDefinition
+from vllm.reasoning import ReasoningParserManager
+from vllm.tool_parsers import ToolParserManager
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+def _build_tokenizer():
+    tokenizer = MagicMock()
+    tokenizer.get_vocab.return_value = {
+        "<think>": 1,
+        "</think>": 2,
+        "<|action_start|>": 3,
+        "<|plugin|>": 4,
+        "<|action_end|>": 5,
+    }
+    return tokenizer
+
+
+def _build_tools() -> list[ChatCompletionToolsParam]:
+    return [
+        ChatCompletionToolsParam(
+            function=FunctionDefinition(
+                name="get_weather",
+                parameters={"type": "object", "properties": {}},
+            )
+        )
+    ]
+
+
+def test_intern_s1_reasoning_parser_registered():
+    parser_cls = ReasoningParserManager.get_reasoning_parser("intern-s1")
+    assert parser_cls.__name__ == "InternS1ReasoningParser"
+
+
+def test_intern_s1_reasoning_parser_hoists_action_to_content():
+    tokenizer = _build_tokenizer()
+    parser = ReasoningParserManager.get_reasoning_parser("intern-s1")(tokenizer)
+    request = ChatCompletionRequest(messages=[], model="test-model")
+
+    reasoning, content = parser.extract_reasoning(
+        "<think>Need tool first.\n"
+        '<|action_start|> <|plugin|>\n{"name": "get_weather", '
+        '"parameters": {"city": "Tokyo"}}\n<|action_end|>\n'
+        "Then continue reasoning.</think>Visible answer.",
+        request,
+    )
+
+    assert reasoning is not None
+    assert "Need tool first." in reasoning
+    assert "Then continue reasoning." in reasoning
+    assert "<|action_start|>" not in reasoning
+    assert content is not None
+    assert content.startswith("<|action_start|> <|plugin|>")
+    assert content.endswith("Visible answer.")
+
+
+def test_intern_s1_reasoning_parser_keeps_deepseek_r1_behavior_without_action():
+    tokenizer = _build_tokenizer()
+    parser = ReasoningParserManager.get_reasoning_parser("intern-s1")(tokenizer)
+    request = ChatCompletionRequest(messages=[], model="test-model")
+
+    reasoning, content = parser.extract_reasoning(
+        "<think>Need to think first.</think>Visible answer.",
+        request,
+    )
+
+    assert reasoning == "Need to think first."
+    assert content == "Visible answer."
+
+
+@pytest.mark.parametrize(
+    "thinking_disabled_request",
+    [
+        ChatCompletionRequest(
+            messages=[],
+            model="test-model",
+            chat_template_kwargs={"enable_thinking": False},
+        ),
+        ChatCompletionRequest(
+            messages=[],
+            model="test-model",
+            enable_thinking=False,
+        ),
+    ],
+)
+def test_intern_s1_reasoning_parser_returns_raw_output_when_thinking_disabled_without_end_token(  # noqa: E501
+    thinking_disabled_request,
+):
+    tokenizer = _build_tokenizer()
+    parser = ReasoningParserManager.get_reasoning_parser("intern-s1")(tokenizer)
+
+    reasoning, content = parser.extract_reasoning(
+        "plain output without end token",
+        thinking_disabled_request,
+    )
+
+    assert reasoning is None
+    assert content == "plain output without end token"
+
+
+@pytest.mark.parametrize(
+    "thinking_disabled_request",
+    [
+        ChatCompletionRequest(
+            messages=[],
+            model="test-model",
+            chat_template_kwargs={"enable_thinking": False},
+        ),
+        ChatCompletionRequest(
+            messages=[],
+            model="test-model",
+            enable_thinking=False,
+        ),
+    ],
+)
+def test_intern_s1_reasoning_parser_splits_reasoning_and_content_when_thinking_disabled_without_action(  # noqa: E501
+    thinking_disabled_request,
+):
+    tokenizer = _build_tokenizer()
+    parser = ReasoningParserManager.get_reasoning_parser("intern-s1")(tokenizer)
+
+    reasoning, content = parser.extract_reasoning(
+        "hidden reasoning</think>visible content",
+        thinking_disabled_request,
+    )
+
+    assert reasoning == "hidden reasoning"
+    assert content == "visible content"
+
+
+@pytest.mark.parametrize(
+    "thinking_disabled_request",
+    [
+        ChatCompletionRequest(
+            messages=[],
+            model="test-model",
+            chat_template_kwargs={"enable_thinking": False},
+        ),
+        ChatCompletionRequest(
+            messages=[],
+            model="test-model",
+            enable_thinking=False,
+        ),
+    ],
+)
+def test_intern_s1_reasoning_parser_hoists_only_action_when_thinking_disabled(
+    thinking_disabled_request,
+):
+    tokenizer = _build_tokenizer()
+    parser = ReasoningParserManager.get_reasoning_parser("intern-s1")(tokenizer)
+
+    reasoning, content = parser.extract_reasoning(
+        "hidden reasoning\n"
+        '<|action_start|> <|plugin|>\n{"name": "get_weather", '
+        '"parameters": {"city": "Tokyo"}}\n<|action_end|>\n'
+        "then more reasoning</think>",
+        thinking_disabled_request,
+    )
+
+    assert reasoning is not None
+    assert "hidden reasoning" in reasoning
+    assert "then more reasoning" in reasoning
+    assert "<|action_start|>" not in reasoning
+    assert content is not None
+    assert content.startswith("<|action_start|> <|plugin|>")
+    assert content.endswith("<|action_end|>")
+
+
+def test_intern_s1_reasoning_and_tool_parser_work_together():
+    tokenizer = _build_tokenizer()
+    tools = _build_tools()
+    request = ChatCompletionRequest(
+        messages=[],
+        model="test-model",
+        tools=tools,
+        tool_choice="auto",
+    )
+
+    reasoning_parser = ReasoningParserManager.get_reasoning_parser("intern-s1")(
+        tokenizer
+    )
+    tool_parser = ToolParserManager.get_tool_parser("intern-s1")(tokenizer, tools)
+
+    reasoning, intermediate_content = reasoning_parser.extract_reasoning(
+        "<think>Need tool first.\n"
+        '<|action_start|> <|plugin|>\n{"name": "get_weather", '
+        '"parameters": {"city": "Tokyo"}}\n<|action_end|>\n'
+        "Then continue reasoning.</think>Visible answer.",
+        request,
+    )
+    extracted = tool_parser.extract_tool_calls(intermediate_content or "", request)
+
+    assert reasoning is not None
+    assert extracted.tools_called is True
+    assert len(extracted.tool_calls) == 1
+    assert extracted.tool_calls[0].function.name == "get_weather"
+    assert extracted.content == "Visible answer."