[Model] Add Reasoning Parser for Granite Models (#14202)

Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com>
Co-authored-by: Joe Runde <joe@joerun.de>

docs/source/features/reasoning_outputs.md

@@ -4,7 +4,7 @@
 
 vLLM offers support for reasoning models like [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), which are designed to generate outputs containing both reasoning steps and final conclusions.
 
-Reasoning models return a additional `reasoning_content` field in their outputs, which contains the reasoning steps that led to the final conclusion. This field is not present in the outputs of other models.
+Reasoning models return an additional `reasoning_content` field in their outputs, which contains the reasoning steps that led to the final conclusion. This field is not present in the outputs of other models.
 
 ## Supported Models
 
@@ -14,6 +14,9 @@ vLLM currently supports the following reasoning models:
 |--------------|-------------|------------------|-------------|
 | [DeepSeek R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) | `deepseek_r1` | `guided_json`, `guided_regex` | ❌ |
 | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | `deepseek_r1` | `guided_json`, `guided_regex` | ✅ |
+| [IBM Granite 3.2 language models](https://huggingface.co/collections/ibm-granite/granite-32-language-models-67b3bc8c13508f6d064cff9a) | `granite` | ❌ | ❌ |
+
+- IBM Granite 3.2 reasoning is disabled by default; to enable it, you must also pass `thinking=True` in your `chat_template_kwargs`.
 
 ## Quickstart
 
@@ -43,6 +46,7 @@ model = models.data[0].id
 
 # Round 1
 messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
+# For granite, add: `extra_body={"chat_template_kwargs": {"thinking": True}}`
 response = client.chat.completions.create(model=model, messages=messages)
 
 reasoning_content = response.choices[0].message.reasoning_content
@@ -97,6 +101,7 @@ models = client.models.list()
 model = models.data[0].id
 
 messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
+# For granite, add: `extra_body={"chat_template_kwargs": {"thinking": True}}`
 stream = client.chat.completions.create(model=model,
                                         messages=messages,
                                         stream=True)

examples/online_serving/openai_chat_completion_with_reasoning.py

@@ -31,6 +31,7 @@ model = models.data[0].id
 
 # Round 1
 messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
+# For granite, add: `extra_body={"chat_template_kwargs": {"thinking": True}}`
 response = client.chat.completions.create(model=model, messages=messages)
 
 reasoning_content = response.choices[0].message.reasoning_content

examples/online_serving/openai_chat_completion_with_reasoning_streaming.py

@@ -38,6 +38,7 @@ models = client.models.list()
 model = models.data[0].id
 
 messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
+# For granite, add: `extra_body={"chat_template_kwargs": {"thinking": True}}`
 stream = client.chat.completions.create(model=model,
                                         messages=messages,
                                         stream=True)

tests/entrypoints/openai/reasoning_parsers/test_granite_reasoning_parser.py

+# SPDX-License-Identifier: Apache-2.0
+import pytest
+from transformers import AutoTokenizer
+
+from tests.entrypoints.openai.reasoning_parsers.utils import (
+    DeltaMessage, run_reasoning_extraction)
+from vllm.entrypoints.openai.reasoning_parsers import (ReasoningParser,
+                                                       ReasoningParserManager)
+
+parser_name = "granite"
+START_REASONING = "Here is my thought process:"
+START_RESPONSE = "Here is my response:"
+
+SIMPLE_REASONING = {
+    "output":
+    f"{START_REASONING}This is a reasoning section{START_RESPONSE}This is the rest",  #noqa: E501
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+}
+COMPLETE_REASONING = {
+    "output": f"{START_REASONING}This is a reasoning section{START_RESPONSE}",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+}
+NO_REASONING = {
+    "output": "This is content",
+    "reasoning_content": None,
+    "content": "This is content",
+}
+MULTIPLE_LINES = {
+    "output":
+    f"{START_REASONING}This\nThat{START_RESPONSE}This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+}
+REASONING_WITH_THINK = {
+    "output":
+    f"{START_REASONING}This is a reasoning section{START_RESPONSE}This is the rest",  #noqa: E501
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+}
+COMPLETE_REASONING_WITH_THINK = {
+    "output": f"{START_REASONING}This is a reasoning section{START_RESPONSE}",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+}
+MULTIPLE_LINES_WITH_THINK = {
+    "output":
+    f"{START_REASONING}This\nThat{START_RESPONSE}This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+}
+
+TEST_CASES = [
+    pytest.param(
+        False,
+        SIMPLE_REASONING,
+        id="simple_reasoning",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING,
+        id="complete_reasoning",
+    ),
+    pytest.param(
+        False,
+        NO_REASONING,
+        id="no_reasoning",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES,
+        id="multiple_lines",
+    ),
+    pytest.param(
+        False,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think",
+    ),
+    pytest.param(
+        True,
+        SIMPLE_REASONING,
+        id="simple_reasoning_streaming",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING,
+        id="complete_reasoning_streaming",
+    ),
+    pytest.param(
+        True,
+        NO_REASONING,
+        id="no_reasoning_streaming",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES,
+        id="multiple_lines_streaming",
+    ),
+    pytest.param(
+        True,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think_streaming",
+    ),
+]
+
+# Global tokenizer initialization to avoid repeated loading
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
+
+
+@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
+def test_reasoning(
+    streaming: bool,
+    param_dict: dict,
+):
+    output = tokenizer.tokenize(param_dict["output"])
+    # decode everything to tokens
+    output_tokens: list[str] = [
+        tokenizer.convert_tokens_to_string([token]) for token in output
+    ]
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
+        parser_name)(tokenizer)
+
+    reasoning, content = run_reasoning_extraction(parser,
+                                                  output_tokens,
+                                                  streaming=streaming)
+
+    assert reasoning == param_dict["reasoning_content"]
+    assert content == param_dict["content"]
+
+
+# Additional tests for verifying the correctness of granite streaming; this
+# is complicated because granite uses multiple tokens to indicate when thinking
+# is starting / when it's starting its response, so skipping special tokens
+# is awkward.
+
+### Handling the start of reasoning
+STREAMING_1 = {
+    "previous_text": None,
+    "current_text": "Here",
+    "delta_text": "Here",
+    "reasoning_content": None,
+    "content": None,
+}
+# When we fail, we should give what was previously being silenced first
+STREAMING_2 = {
+    "previous_text": "Here is my thought",
+    "current_text": "Here is my thought failure",
+    "delta_text": " failure",
+    "reasoning_content": None,
+    "content": "Here is my thought failure",
+}
+# But then after the first one, we should only add the delta text to content
+STREAMING_3 = {
+    "previous_text": "Here wrong",
+    "current_text": " words",
+    "delta_text": " Here wrong words",
+    "reasoning_content": None,
+    "content": " words",
+}
+# But then after the first one, we should only add the delta text to content
+STREAMING_4 = {
+    "previous_text": "Here is my thought",
+    "current_text": "Here is my thought process:",
+    "delta_text": " process:",
+    "reasoning_content": None,
+    "content": None,
+}
+# Reasoning started successfully; parse reasoning content
+STREAMING_5 = {
+    "previous_text": "Here is my thought process:",
+    "current_text": "Here is my thought process: foo",
+    "delta_text": " foo",
+    "reasoning_content": " foo",
+    "content": None,
+}
+# Response special sequence has started, but not finished.
+STREAMING_6 = {
+    "previous_text": "Here is my thought process: foo",
+    "current_text": "Here is my thought process: foo Here is",
+    "delta_text": " Here is",
+    "reasoning_content": " ",
+    "content": None,
+}
+# Response special sequence started, but was broken; the reasoning
+# content should be the content that was previously unused.
+STREAMING_7 = {
+    "previous_text": "Here is my thought process: foo Here is",
+    "current_text": "Here is my thought process: foo Here is Here",
+    "delta_text": " Here",
+    "reasoning_content": "Here is ",
+    "content": None,
+}
+# Response special sequence is ongoing
+STREAMING_8 = {
+    "previous_text": "Here is my thought process: foo Here is my response:",
+    "current_text": "Here is my thought process: foo Here is my response: bar",
+    "delta_text": " bar",
+    "reasoning_content": None,
+    "content": " bar",
+}
+# The delta text has everything; we should be able to correctly parse both
+STREAMING_9 = {
+    "previous_text": None,
+    "current_text": "Here is my thought process: foo Here is my response: bar",
+    "delta_text": "Here is my thought process: foo Here is my response: bar",
+    "reasoning_content": " foo ",
+    "content": " bar",
+}
+## The Response is ongoing, and the delta mixes reasoning content / content
+STREAMING_10 = {
+    "previous_text": "Here is my thought process: foo",
+    "current_text":
+    "Here is my thought process: foo bar Here is my response: baz",
+    "delta_text": " bar Here is my response: baz",
+    "reasoning_content": " bar ",
+    "content": " baz",
+}
+# The delta text starts a new substring that might be a response special seq
+STREAMING_11 = {
+    "previous_text":
+    "Here is my thought process: This is a reasoning section ",
+    "current_text":
+    "Here is my thought process: This is a reasoning section Here",
+    "delta_text": "Here",
+    "reasoning_content": None,
+    "content": None,
+}
+# The delta text is finishing the response special seq
+STREAMING_12 = {
+    "previous_text": "Here is my thought process: foo Here is my response",
+    "current_text": "Here is my thought process: foo Here is my response:",
+    "delta_text": ":",
+    "reasoning_content": None,
+    "content": None,
+}
+STREAMING_13 = {
+    "previous_text": "Here is my thought process: foo Here",
+    "current_text": "Here is my thought process: foo Here was",
+    "delta_text": " was",
+    "reasoning_content": "Here was",
+    "content": None,
+}
+
+STREAMING_SUBCASES = [
+    pytest.param(
+        STREAMING_1,
+        id="Starting reasoning special sequence",
+    ),
+    pytest.param(
+        STREAMING_2,
+        id="Unexpected start reasoning sequence",
+    ),
+    pytest.param(
+        STREAMING_3,
+        id="Continuing unexpected start reasoning sequence",
+    ),
+    pytest.param(
+        STREAMING_4,
+        id="Only start reasoning sequence and nothing else",
+    ),
+    pytest.param(
+        STREAMING_5,
+        id="Reasoning content has started",
+    ),
+    pytest.param(
+        STREAMING_6,
+        id="Response special sequence has started",
+    ),
+    pytest.param(
+        STREAMING_7,
+        id="Response special sequence reset",
+    ),
+    pytest.param(
+        STREAMING_8,
+        id="Response text has started",
+    ),
+    pytest.param(
+        STREAMING_9,
+        id="Delta contains everything",
+    ),
+    pytest.param(
+        STREAMING_10,
+        id="Delta contains some reasoning and response",
+    ),
+    pytest.param(
+        STREAMING_11,
+        id="Delta starts response sequence",
+    ),
+    pytest.param(
+        STREAMING_12,
+        id="Delta finishes response sequence",
+    ),
+    pytest.param(
+        STREAMING_13,
+        id="Delta breaks potential responise sequence",
+    ),
+]
+
+
+@pytest.mark.parametrize("param_dict", STREAMING_SUBCASES)
+def test_streaming_subcases(param_dict):
+    # Get all of the token IDs
+    previous_token_ids = tokenizer.encode(
+        param_dict["previous_text"]
+    ) if param_dict["previous_text"] is not None else []
+    current_token_ids = tokenizer.encode(param_dict["current_text"])
+    delta_token_ids = tokenizer.encode(param_dict["delta_text"])
+
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
+        parser_name)(tokenizer)
+
+    response = parser.extract_reasoning_content_streaming(
+        previous_text=param_dict["previous_text"],
+        current_text=param_dict["current_text"],
+        delta_text=param_dict["delta_text"],
+        previous_token_ids=previous_token_ids,
+        current_token_ids=current_token_ids,
+        delta_token_ids=delta_token_ids,
+    )
+    # Streaming currently expects at least one of reasoning content / content,
+    # so the response should return None in that case.
+    if param_dict["reasoning_content"] is None and param_dict[
+            "content"] is None:
+        assert response is None
+    else:
+        assert isinstance(response, DeltaMessage)
+        assert param_dict["reasoning_content"] == response.reasoning_content
+        assert param_dict["content"] == response.content

vllm/engine/arg_utils.py

@@ -1099,7 +1099,7 @@ class EngineArgs:
         parser.add_argument(
             "--reasoning-parser",
             type=str,
-            choices=["deepseek_r1"],
+            choices=["deepseek_r1", "granite"],
             default=None,
             help=
             "Select the reasoning parser depending on the model that you're "

vllm/entrypoints/openai/reasoning_parsers/__init__.py

@@ -2,7 +2,11 @@
 
 from .abs_reasoning_parsers import ReasoningParser, ReasoningParserManager
 from .deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+from .granite_reasoning_parser import GraniteReasoningParser
 
 __all__ = [
-    "ReasoningParser", "ReasoningParserManager", "DeepSeekR1ReasoningParser"
+    "ReasoningParser",
+    "ReasoningParserManager",
+    "DeepSeekR1ReasoningParser",
+    "GraniteReasoningParser",
 ]

vllm/entrypoints/openai/reasoning_parsers/granite_reasoning_parser.py

+# SPDX-License-Identifier: Apache-2.0
+
+import re
+from collections.abc import Sequence
+from typing import Optional, Union
+
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaMessage)
+from vllm.entrypoints.openai.reasoning_parsers.abs_reasoning_parsers import (
+    ReasoningParser, ReasoningParserManager)
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@ReasoningParserManager.register_module("granite")
+class GraniteReasoningParser(ReasoningParser):
+    """
+    Reasoning parser for IBM Granite.
+
+    IBM granite models currently use "Here is my thought process:"
+    and "Here is my response:" to separate its thinking / response outputs.
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase):
+        super().__init__(tokenizer)
+
+        # NOTE: There have been some observed occurrences of quantized
+        # instances of the current models using "Here's" instead of "Here is",
+        # so to be safe, we match on both.
+        self.think_start_expr = r"(?:Here's|Here is) my thought process:"
+        self.response_start_expr = r"(?:Here's|Here is) my response:"
+
+        self.reasoning_regex = re.compile(
+            rf"{self.think_start_expr}(.*?){self.response_start_expr}(.*)",
+            re.DOTALL)
+
+        self.valid_think_starts = [
+            "Here's my thought process:", "Here is my thought process:"
+        ]
+        self.valid_response_starts = [
+            "Here's my response:", "Here is my response:"
+        ]
+
+        # Substrings to match for sequence boundaries on raw text
+        self.seq_boundary_end = ":"
+        self.seq_boundary_start = "Here"
+
+        # The longest any thinking / start of response message can be
+        self.longest_think_start = max(
+            len(think_start) for think_start in self.valid_think_starts)
+
+    def extract_reasoning_content(
+            self, model_output: str, request: ChatCompletionRequest
+    ) -> tuple[Optional[str], Optional[str]]:
+        """Extract the reasoning content & content sections, respectively.
+        If the sequence doesn't match what we expect, i.e., the model generates
+        something else, all content is considered non-reasoning content.
+
+        Args:
+            model_output (str): Output of the model to be parsed.
+            request (ChatCompletionReqest): Request being processed.
+
+        Returns:
+            tuple[Optional[str], Optional[str]]: Tuple pair containing the
+            reasoning content and non-reasoning content.
+        """
+        re_match = self.reasoning_regex.findall(model_output)
+        if not re_match:
+            return None, model_output
+        reasoning_content, response_content = re_match[0]
+        if not response_content:
+            return reasoning_content, None
+        return reasoning_content, response_content
+
+    def extract_reasoning_content_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],
+    ) -> Union[DeltaMessage, None]:
+        """Extract the reasoning content / content emitted by granite models;
+        If the sequence doesn't match what we expect, i.e., the model generates
+        something else, all content is considered non-reasoning content.
+
+        NOTE: Granite models do not use a special token to start their reasoning
+        and response sections; instead they have token sequences, e.g.,
+
+                Here is my thought process: Foo Here is my response: Bar
+
+        This increases the complexity of correctly handling streams, since we
+        need to watch for specific sequences and correctly parse them without
+        dropping content that is potentially overlapping & spanning multiple
+        delta messages.
+
+        Args:
+            previous_text (str): Previous text outside of this delta message.
+            current_text (str): Previous text + delta text.
+            delta_text (str): Text to consider and parse content from.
+            previous_token_ids (Sequence[int]): Token IDs of previous_text.
+            current_token_ids (Sequence[int]): Token IDs of current_text.
+            delta_token_ids (Sequence[int]): Token IDs of delta_text.
+
+        Returns:
+            Union[DeltaMessage, None]
+                DeltaMessage with either reasoning content or content, or None.
+        """
+        reasoning_content, resp_seq_len, content = self._get_content_sections(
+            current_text)
+        # Either we haven't finished the start of the reasoning sequence,
+        # or the model is generating something unexpected.
+        if not reasoning_content:
+            delta_message = self._get_delta_message_with_no_reasoning_bounds(
+                current_text, delta_text)
+        # We have a start of reasoning message, but have not yet finished
+        # the start of response sequence.
+        elif not content:
+            delta_message = self._get_delta_message_with_no_response_bounds(
+                current_text, reasoning_content, delta_text)
+        # We've finished both the start of reasoning and start of response seq.
+        else:
+            # This should never happen since we matched on the response
+            assert resp_seq_len is not None
+            delta_message = self._get_delta_message_with_both_bounds(
+                delta_text, reasoning_content, content, current_text,
+                resp_seq_len)
+        if not delta_message.content and not delta_message.reasoning_content:
+            return None
+        return delta_message
+
+    #### Implementation details of stream parsing for granite models
+    def _is_reasoning_start_substr(self, text: str) -> bool:
+        """Check if a text matches one of the possible start reasoning seqs.
+
+        Args:
+            text (str): Text to check for leading substr.
+        
+        Returns:
+            bool: True if any of the possible reasoning start seqs match.
+        """
+        return any(
+            think_start.startswith(text)
+            for think_start in self.valid_think_starts)
+
+    def _is_response_start_substr(self, text: str) -> bool:
+        """Check if a text matches one of the possible start response seqs.
+
+        Args:
+            text (str): Text to check for leading substr.
+        
+        Returns:
+            bool: True if any of the possible response start seqs match.
+        """
+        return any(
+            response_start.startswith(text)
+            for response_start in self.valid_response_starts)
+
+    def _get_delta_message_with_no_reasoning_bounds(
+        self,
+        current_text: str,
+        delta_text: str,
+    ) -> DeltaMessage:
+        """Parse the delta message when the current text has not yet completed
+        its start of reasoning sequence.
+
+        Args:
+            current_text (str): The full previous + delta text.
+            delta_text (str): Text to consider and parse content from.
+
+        Returns:
+            DeltaMessage: Message containing the parsed content.
+        """
+        prev_longest_length = len(current_text) - len(delta_text)
+        is_substr = self._is_reasoning_start_substr(current_text)
+        was_substr = self._is_reasoning_start_substr(
+            current_text[:prev_longest_length])
+
+        # Check if we just generated something NOT in the special token seq;
+        # if so, add everything that we previously skipped with this delta
+        # message and append everything to content in the future.
+        if was_substr and not is_substr:
+            return DeltaMessage(
+                reasoning_content=None,
+                content=current_text,
+            )
+        if is_substr:
+            # Might still be in the special token sequence; return nothing
+            return DeltaMessage(reasoning_content=None, content=None)
+        # Otherwise the sequence has already been broken and we already
+        # corrected; just return the delta text as normal content.
+        return DeltaMessage(reasoning_content=None, content=delta_text)
+
+    def _get_delta_message_with_no_response_bounds(
+        self,
+        current_text: str,
+        reasoning_content: str,
+        delta_text: str,
+    ) -> DeltaMessage:
+        """Parse the delta message when the current text has both reasoning
+        content with no (response) content. NOTE that we may have overlapping
+        tokens with the start of reasoning / start of response sequences on
+        either side of the delta text.
+
+        Args:
+            current_text (str): The full previous + delta text.
+            reasoning_content (str): reasoning content from current_text.
+            delta_text (str): Text to consider and parse content from.
+
+        Returns:
+            DeltaMessage: Message containing the parsed content.
+        """
+        # If we have no reasoning content or explicitly end with the start of
+        # response sequence, we are in transition to the response; need to be
+        # careful here, since the final token (:) will match the reasoning
+        # content and fully parse it out; we should not pass the : back.
+        ends_with_start_response_seq = any(
+            current_text.endswith(response_start)
+            for response_start in self.valid_response_starts)
+        if reasoning_content is None or ends_with_start_response_seq:
+            return DeltaMessage(reasoning_content=None, content=None)
+
+        # Consider previous / current text only within context of the reasoning
+        previous_text = reasoning_content[:-len(delta_text)]
+        current_text = reasoning_content
+
+        # We need to be careful about adding unfinished response sequences;
+        # Find the place at which we MIGHT be starting a response sequence
+        prev_idx = previous_text.rfind(self.seq_boundary_start)
+        delta_idx = delta_text.rfind(self.seq_boundary_start)
+
+        # Check the state of potential start of response substring matches.
+        prev_was_substr = self._is_response_start_substr(
+            previous_text[prev_idx:]) if prev_idx >= 0 else False
+        delta_continues_substr = self._is_response_start_substr(
+            current_text[prev_idx:]) if prev_idx >= 0 else False
+        delta_new_substr = self._is_response_start_substr(
+            delta_text[delta_idx:]) if delta_idx >= 0 else False
+
+        # Delta only contains potential continued response sequence text.
+        if delta_continues_substr:
+            return DeltaMessage(reasoning_content=None, content=None)
+
+        if not prev_was_substr:
+            # Delta may be starting a new response seq but has other text too.
+            if delta_new_substr:
+                return DeltaMessage(reasoning_content=delta_text[:delta_idx],
+                                    content=None)
+            # Normal case for most reasoning text (no potential special seqs).
+            return DeltaMessage(reasoning_content=delta_text, content=None)
+        # The substring that previously seemed to be a potential response
+        # seq wasn't one; we need to add the content to the delta message,
+        # and also slice off the potential response sequence
+        elif delta_new_substr:
+            reasoning_content = previous_text[
+                prev_idx:] + delta_text[:delta_idx]
+            return DeltaMessage(reasoning_content=reasoning_content,
+                                content=None)
+        # No new substring yet, and we broke our old one; take the whole delta
+        return DeltaMessage(
+            reasoning_content=previous_text[prev_idx:] + delta_text,
+            content=None,
+        )
+
+    def _get_delta_message_with_both_bounds(
+        self,
+        delta_text: str,
+        reasoning_content: str,
+        response_content: str,
+        current_text: str,
+        response_seq_len: int,
+    ) -> DeltaMessage:
+        """Parse the delta message when the current text has both reasoning
+        content and normal (response) content.
+
+        Args:
+            delta_text (str): Text to consider and parse content from.
+            reasoning_content (str): reasoning content from current_text.
+            response_content (str): response content from current_text.
+            current_text (str): The full previous + delta text.
+            response_seq_len(str): Len of the complete response sequence used.
+
+        Returns:
+            DeltaMessage: Message containing the parsed content.
+        """
+        # Always have content; take length to the end
+        delta_content = delta_text[-len(response_content):]
+        reasoning_end_idx = len(delta_text) - (len(response_content) +
+                                               response_seq_len)
+
+        if reasoning_end_idx < 0:
+            delta_reasoning_content = None
+        else:
+            # Get the starting offset
+            start_reasoning_content_idx = len(
+                reasoning_content) + response_seq_len + len(
+                    response_content) - 1
+            delta_offset = len(current_text) - len(delta_text)
+            start_offset = start_reasoning_content_idx - delta_offset
+            if start_offset < 0:
+                start_offset = 0
+            delta_reasoning_content = delta_text[
+                start_offset:reasoning_end_idx]
+
+        return DeltaMessage(
+            reasoning_content=delta_reasoning_content,
+            content=delta_content,
+        )
+
+    def _get_content_sections(
+        self, current_text: str
+    ) -> tuple[Optional[str], Optional[int], Optional[str]]:
+        """Parse the text to extract the reasoning content / content
+        if we have them.
+
+        Args:
+            current_text (str): The full previous + delta text.
+
+        Returns:
+            tuple[Optional[str], Optional[int], Optional[str]]: Tuple of len 3
+            containing the reasoning content, the length of the response seq
+            (if there is one) and the non-reasoning content.
+        """
+        current_chunk_start = 0
+        start_reasoning_content = None
+        parsed_content = False
+        delimiter_idxs = [
+            idx for idx, char in enumerate(current_text)
+            if char == self.seq_boundary_end
+        ]
+
+        for current_chunk_end in delimiter_idxs:
+            current_chunk = current_text[current_chunk_start:current_chunk_end]
+            # Check to see if the start of reasoning seq if complete
+            if start_reasoning_content is None:
+                for think_start in self.valid_think_starts:
+                    if current_chunk == think_start[:-1]:
+                        start_reasoning_content = current_chunk_end + 1
+                        current_chunk_start = current_chunk_end + 1
+                        break
+
+            # Check to see if the start of response seq if complete
+            elif not parsed_content:
+                for response_start in self.valid_response_starts:
+                    if current_chunk[-len(response_start) +
+                                     1:] == response_start[:-1]:
+                        # Mark end of reasoning and start response content
+                        # after the start of response sequence.
+                        end_reasoning_content = current_chunk_end - len(
+                            response_start)
+                        reasoning_content = current_text[
+                            start_reasoning_content:end_reasoning_content]
+                        response_content = current_text[current_chunk_end + 1:]
+                        return reasoning_content, len(
+                            response_start), response_content
+
+        if start_reasoning_content and not parsed_content:
+            return current_text[start_reasoning_content:], None, None
+        return None, None, None

vllm/model_executor/guided_decoding/reasoner/__init__.py

@@ -19,6 +19,10 @@ def get_reasoner(tokenizer: PreTrainedTokenizer,
         return None
     elif reasoning_backend == "deepseek_r1":
         return DeepSeekReasoner.from_tokenizer(tokenizer)
+    elif reasoning_backend == "granite":
+        logger.warning(
+            "Granite reasoner not yet implemented for structured outputs")
+        return None
     else:
         # Raise a warning for unknown reasoning backend and return None
         # We cannot raise an error here because some reasoning models