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Unverified Commit 6299628d authored by Rei.'s avatar Rei. Committed by GitHub
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[bugfix] fix MiniMaxM2ReasoningParser streaming output not separating reasoning_content. (#29882) 


Signed-off-by: default avatarRei <1477174254@qq.com>
parent fba89069
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tests/reasoning/test_minimax_m2_append_reasoning_parser.py 0 → 100644
View file @ 6299628d
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from transformers import AutoTokenizer
from tests.reasoning.utils import run_reasoning_extraction
from vllm.reasoning import ReasoningParser, ReasoningParserManager
parser_name = "minimax_m2_append_think"
end_token = "</think>"
# MiniMax M2 model path
REASONING_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
@pytest.fixture(scope="module")
def minimax_m2_tokenizer():
return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
# =============================================================================
# MiniMaxM2AppendThinkReasoningParser behavior:
# - Prepends <think> to the beginning of the output
# - Does NOT separate reasoning and content
# - Returns everything as content (with <think> prepended)
# - reasoning is always None
#
# This parser is used when you want to keep the raw output with <think> added
# =============================================================================
# Case: simple output with end token
SIMPLE_OUTPUT = {
"output": "This is reasoning</think>This is response",
"reasoning": None,
"content": "<think>This is reasoning</think>This is response",
"is_reasoning_end": True,
}
# Case: output without end token (reasoning in progress)
NO_END_TOKEN = {
"output": "This is reasoning in progress",
"reasoning": None,
"content": "<think>This is reasoning in progress",
"is_reasoning_end": False,
}
# Case: only end token
ONLY_END_TOKEN = {
"output": "</think>This is response",
"reasoning": None,
"content": "<think></think>This is response",
"is_reasoning_end": True,
}
# Case: multiple lines
MULTIPLE_LINES = {
"output": "Line 1\nLine 2</think>Response 1\nResponse 2",
"reasoning": None,
"content": "<think>Line 1\nLine 2</think>Response 1\nResponse 2",
"is_reasoning_end": True,
}
# Case: empty output (non-streaming prepends <think>)
EMPTY = {
"output": "",
"reasoning": None,
"content": "<think>",
"is_reasoning_end": False,
}
# Case: empty output streaming (no tokens = no output)
EMPTY_STREAMING = {
"output": "",
"reasoning": None,
"content": None,
"is_reasoning_end": False,
}
# Case: special characters
SPECIAL_CHARS = {
"output": "Let me think... 1+1=2</think>Yes!",
"reasoning": None,
"content": "<think>Let me think... 1+1=2</think>Yes!",
"is_reasoning_end": True,
}
# Case: code in output
CODE_OUTPUT = {
"output": "```python\nprint('hi')\n```</think>Here's the code.",
"reasoning": None,
"content": "<think>```python\nprint('hi')\n```</think>Here's the code.",
"is_reasoning_end": True,
}
TEST_CASES = [
pytest.param(
False,
SIMPLE_OUTPUT,
id="simple_output",
),
pytest.param(
True,
SIMPLE_OUTPUT,
id="simple_output_streaming",
),
pytest.param(
False,
NO_END_TOKEN,
id="no_end_token",
),
pytest.param(
True,
NO_END_TOKEN,
id="no_end_token_streaming",
),
pytest.param(
False,
ONLY_END_TOKEN,
id="only_end_token",
),
pytest.param(
True,
ONLY_END_TOKEN,
id="only_end_token_streaming",
),
pytest.param(
False,
MULTIPLE_LINES,
id="multiple_lines",
),
pytest.param(
True,
MULTIPLE_LINES,
id="multiple_lines_streaming",
),
pytest.param(
False,
EMPTY,
id="empty",
),
pytest.param(
True,
EMPTY_STREAMING,
id="empty_streaming",
),
pytest.param(
False,
SPECIAL_CHARS,
id="special_chars",
),
pytest.param(
True,
SPECIAL_CHARS,
id="special_chars_streaming",
),
pytest.param(
False,
CODE_OUTPUT,
id="code_output",
),
pytest.param(
True,
CODE_OUTPUT,
id="code_output_streaming",
),
]
@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
def test_reasoning(
streaming: bool,
param_dict: dict,
minimax_m2_tokenizer,
):
output = minimax_m2_tokenizer.tokenize(param_dict["output"])
# decode everything to tokens
output_tokens: list[str] = [
minimax_m2_tokenizer.convert_tokens_to_string([token]) for token in output
]
parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
minimax_m2_tokenizer
)
reasoning, content = run_reasoning_extraction(
parser, output_tokens, streaming=streaming
)
assert reasoning == param_dict["reasoning"]
assert content == param_dict["content"]
# Test is_reasoning_end
output_ids = minimax_m2_tokenizer.convert_tokens_to_ids(output)
is_reasoning_end = parser.is_reasoning_end(output_ids)
assert is_reasoning_end == param_dict["is_reasoning_end"]
tests/reasoning/test_minimax_m2_reasoning_parser.py 0 → 100644
View file @ 6299628d
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from transformers import AutoTokenizer
from tests.reasoning.utils import run_reasoning_extraction
from vllm.reasoning import ReasoningParser, ReasoningParserManager
parser_name = "minimax_m2"
end_token = "</think>"
# MiniMax M2 model path
REASONING_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
@pytest.fixture(scope="module")
def minimax_m2_tokenizer():
return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
# =============================================================================
# MiniMax M2 specific behavior:
# - Model does NOT generate <think> start token
# - Model only generates </think> end token
# - All content before </think> is reasoning
# - All content after </think> is the actual response (content)
# =============================================================================
# Case: reasoning + end token + content (typical case)
SIMPLE_REASONING = {
"output": "This is a reasoning section</think>This is the rest",
"reasoning": "This is a reasoning section",
"content": "This is the rest",
"is_reasoning_end": True,
}
# Case: reasoning + end token only (no content after)
COMPLETE_REASONING = {
"output": "This is a reasoning section</think>",
"reasoning": "This is a reasoning section",
"content": None,
"is_reasoning_end": True,
}
# Case: no end token yet (streaming in progress, all is reasoning)
NO_END_TOKEN = {
"output": "This is reasoning in progress",
"reasoning": "This is reasoning in progress",
"content": None,
"is_reasoning_end": False,
}
# Case: multiple lines of reasoning
MULTIPLE_LINES = {
"output": "First line\nSecond line</think>Response first line\nResponse second",
"reasoning": "First line\nSecond line",
"content": "Response first line\nResponse second",
"is_reasoning_end": True,
}
# Case: only end token (empty reasoning, immediate response)
SHORTEST_REASONING_NO_STREAMING = {
"output": "</think>This is the response",
"reasoning": "",
"content": "This is the response",
"is_reasoning_end": True,
}
# Case: only end token streaming (reasoning is None because it's just the token)
SHORTEST_REASONING_STREAMING = {
"output": "</think>This is the response",
"reasoning": None,
"content": "This is the response",
"is_reasoning_end": True,
}
# Case: empty output
EMPTY = {
"output": "",
"reasoning": "",
"content": None,
"is_reasoning_end": False,
}
# Case: empty streaming
EMPTY_STREAMING = {
"output": "",
"reasoning": None,
"content": None,
"is_reasoning_end": False,
}
# Case: long reasoning with special characters
SPECIAL_CHARS = {
"output": "Let me think... 1+1=2, right?</think>Yes, 1+1=2.",
"reasoning": "Let me think... 1+1=2, right?",
"content": "Yes, 1+1=2.",
"is_reasoning_end": True,
}
# Case: reasoning with code blocks
CODE_IN_REASONING = {
"output": "```python\nprint('hello')\n```</think>Here is the code.",
"reasoning": "```python\nprint('hello')\n```",
"content": "Here is the code.",
"is_reasoning_end": True,
}
TEST_CASES = [
# Core cases: no start token (MiniMax M2 actual behavior)
pytest.param(
False,
SIMPLE_REASONING,
id="simple_reasoning",
),
pytest.param(
True,
SIMPLE_REASONING,
id="simple_reasoning_streaming",
),
pytest.param(
False,
COMPLETE_REASONING,
id="complete_reasoning",
),
pytest.param(
True,
COMPLETE_REASONING,
id="complete_reasoning_streaming",
),
pytest.param(
False,
NO_END_TOKEN,
id="no_end_token",
),
pytest.param(
True,
NO_END_TOKEN,
id="no_end_token_streaming",
),
pytest.param(
False,
MULTIPLE_LINES,
id="multiple_lines",
),
pytest.param(
True,
MULTIPLE_LINES,
id="multiple_lines_streaming",
),
pytest.param(
False,
SHORTEST_REASONING_NO_STREAMING,
id="shortest_reasoning",
),
pytest.param(
True,
SHORTEST_REASONING_STREAMING,
id="shortest_reasoning_streaming",
),
pytest.param(
False,
EMPTY,
id="empty",
),
pytest.param(
True,
EMPTY_STREAMING,
id="empty_streaming",
),
pytest.param(
False,
SPECIAL_CHARS,
id="special_chars",
),
pytest.param(
True,
SPECIAL_CHARS,
id="special_chars_streaming",
),
pytest.param(
False,
CODE_IN_REASONING,
id="code_in_reasoning",
),
pytest.param(
True,
CODE_IN_REASONING,
id="code_in_reasoning_streaming",
),
]
@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
def test_reasoning(
streaming: bool,
param_dict: dict,
minimax_m2_tokenizer,
):
output = minimax_m2_tokenizer.tokenize(param_dict["output"])
# decode everything to tokens
output_tokens: list[str] = [
minimax_m2_tokenizer.convert_tokens_to_string([token]) for token in output
]
parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
minimax_m2_tokenizer
)
reasoning, content = run_reasoning_extraction(
parser, output_tokens, streaming=streaming
)
assert reasoning == param_dict["reasoning"]
assert content == param_dict["content"]
# Test is_reasoning_end
output_ids = minimax_m2_tokenizer.convert_tokens_to_ids(output)
is_reasoning_end = parser.is_reasoning_end(output_ids)
assert is_reasoning_end == param_dict["is_reasoning_end"]
# Test extract_content
if param_dict["content"] is not None:
content = parser.extract_content_ids(output_ids)
assert content == minimax_m2_tokenizer.convert_tokens_to_ids(
minimax_m2_tokenizer.tokenize(param_dict["content"])
)
else:
content = parser.extract_content_ids(output)
assert content == []
vllm/reasoning/minimax_m2_reasoning_parser.py
View file @ 6299628d
......@@ -19,6 +19,10 @@ logger = init_logger(__name__)
class MiniMaxM2ReasoningParser(BaseThinkingReasoningParser):
"""
Reasoning parser for MiniMax M2 model.
MiniMax M2 models don't generate <think> start token, only </think> end
token. All content before </think> is reasoning, content after is the
actual response.
"""
@property
......@@ -31,6 +35,45 @@ class MiniMaxM2ReasoningParser(BaseThinkingReasoningParser):
"""The token that ends reasoning content."""
return "</think>"
def extract_reasoning_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],
) -> DeltaMessage | None:
"""
Extract reasoning content from a delta message for streaming.
MiniMax M2 models don't generate <think> start token, so we assume
all content is reasoning until we encounter the </think> end token.
"""
# Skip single end token
if len(delta_token_ids) == 1 and delta_token_ids[0] == self.end_token_id:
return None
# Check if end token has already appeared in previous tokens
# meaning we're past the reasoning phase
if self.end_token_id in previous_token_ids:
# We're past the reasoning phase, this is content
return DeltaMessage(content=delta_text)
# Check if end token is in delta tokens
if self.end_token_id in delta_token_ids:
# End token in delta, split reasoning and content
end_index = delta_text.find(self.end_token)
reasoning = delta_text[:end_index]
content = delta_text[end_index + len(self.end_token) :]
return DeltaMessage(
reasoning=reasoning if reasoning else None,
content=content if content else None,
)
# No end token yet, all content is reasoning
return DeltaMessage(reasoning=delta_text)
class MiniMaxM2AppendThinkReasoningParser(ReasoningParser):
"""
......
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