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[bugfix] fix MiniMaxM2ReasoningParser streaming output not separating reasoning_content. (#29882)
Signed-off-by: Rei <1477174254@qq.com>
This commit is contained in:
Rei.
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195
tests/reasoning/test_minimax_m2_append_reasoning_parser.py
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195
tests/reasoning/test_minimax_m2_append_reasoning_parser.py
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@ -0,0 +1,195 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from transformers import AutoTokenizer
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from tests.reasoning.utils import run_reasoning_extraction
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from vllm.reasoning import ReasoningParser, ReasoningParserManager
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parser_name = "minimax_m2_append_think"
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end_token = "</think>"
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# MiniMax M2 model path
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REASONING_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
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@pytest.fixture(scope="module")
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def minimax_m2_tokenizer():
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return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
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# =============================================================================
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# MiniMaxM2AppendThinkReasoningParser behavior:
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# - Prepends <think> to the beginning of the output
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# - Does NOT separate reasoning and content
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# - Returns everything as content (with <think> prepended)
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# - reasoning is always None
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#
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# This parser is used when you want to keep the raw output with <think> added
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# =============================================================================
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# Case: simple output with end token
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SIMPLE_OUTPUT = {
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"output": "This is reasoning</think>This is response",
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"reasoning": None,
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"content": "<think>This is reasoning</think>This is response",
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"is_reasoning_end": True,
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}
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# Case: output without end token (reasoning in progress)
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NO_END_TOKEN = {
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"output": "This is reasoning in progress",
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"reasoning": None,
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"content": "<think>This is reasoning in progress",
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"is_reasoning_end": False,
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}
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# Case: only end token
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ONLY_END_TOKEN = {
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"output": "</think>This is response",
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"reasoning": None,
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"content": "<think></think>This is response",
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"is_reasoning_end": True,
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}
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# Case: multiple lines
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MULTIPLE_LINES = {
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"output": "Line 1\nLine 2</think>Response 1\nResponse 2",
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"reasoning": None,
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"content": "<think>Line 1\nLine 2</think>Response 1\nResponse 2",
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"is_reasoning_end": True,
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}
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# Case: empty output (non-streaming prepends <think>)
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EMPTY = {
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"output": "",
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"reasoning": None,
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"content": "<think>",
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"is_reasoning_end": False,
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}
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# Case: empty output streaming (no tokens = no output)
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EMPTY_STREAMING = {
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"output": "",
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"reasoning": None,
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"content": None,
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"is_reasoning_end": False,
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}
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# Case: special characters
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SPECIAL_CHARS = {
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"output": "Let me think... 1+1=2</think>Yes!",
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"reasoning": None,
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"content": "<think>Let me think... 1+1=2</think>Yes!",
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"is_reasoning_end": True,
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}
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# Case: code in output
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CODE_OUTPUT = {
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"output": "```python\nprint('hi')\n```</think>Here's the code.",
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"reasoning": None,
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"content": "<think>```python\nprint('hi')\n```</think>Here's the code.",
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"is_reasoning_end": True,
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}
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TEST_CASES = [
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pytest.param(
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False,
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SIMPLE_OUTPUT,
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id="simple_output",
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),
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pytest.param(
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True,
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SIMPLE_OUTPUT,
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id="simple_output_streaming",
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),
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pytest.param(
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False,
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NO_END_TOKEN,
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id="no_end_token",
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),
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pytest.param(
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True,
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NO_END_TOKEN,
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id="no_end_token_streaming",
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),
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pytest.param(
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False,
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ONLY_END_TOKEN,
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id="only_end_token",
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),
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pytest.param(
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True,
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ONLY_END_TOKEN,
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id="only_end_token_streaming",
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),
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pytest.param(
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False,
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MULTIPLE_LINES,
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id="multiple_lines",
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),
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pytest.param(
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True,
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MULTIPLE_LINES,
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id="multiple_lines_streaming",
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),
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pytest.param(
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False,
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EMPTY,
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id="empty",
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),
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pytest.param(
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True,
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EMPTY_STREAMING,
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id="empty_streaming",
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),
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pytest.param(
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False,
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SPECIAL_CHARS,
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id="special_chars",
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),
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pytest.param(
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True,
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SPECIAL_CHARS,
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id="special_chars_streaming",
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),
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pytest.param(
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False,
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CODE_OUTPUT,
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id="code_output",
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),
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pytest.param(
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True,
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CODE_OUTPUT,
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id="code_output_streaming",
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),
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]
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@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
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def test_reasoning(
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streaming: bool,
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param_dict: dict,
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minimax_m2_tokenizer,
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):
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output = minimax_m2_tokenizer.tokenize(param_dict["output"])
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# decode everything to tokens
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output_tokens: list[str] = [
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minimax_m2_tokenizer.convert_tokens_to_string([token]) for token in output
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]
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parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
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minimax_m2_tokenizer
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)
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reasoning, content = run_reasoning_extraction(
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parser, output_tokens, streaming=streaming
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)
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assert reasoning == param_dict["reasoning"]
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assert content == param_dict["content"]
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# Test is_reasoning_end
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output_ids = minimax_m2_tokenizer.convert_tokens_to_ids(output)
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is_reasoning_end = parser.is_reasoning_end(output_ids)
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assert is_reasoning_end == param_dict["is_reasoning_end"]
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230
tests/reasoning/test_minimax_m2_reasoning_parser.py
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230
tests/reasoning/test_minimax_m2_reasoning_parser.py
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@ -0,0 +1,230 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from transformers import AutoTokenizer
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from tests.reasoning.utils import run_reasoning_extraction
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from vllm.reasoning import ReasoningParser, ReasoningParserManager
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parser_name = "minimax_m2"
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end_token = "</think>"
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# MiniMax M2 model path
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REASONING_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
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@pytest.fixture(scope="module")
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def minimax_m2_tokenizer():
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return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
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# =============================================================================
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# MiniMax M2 specific behavior:
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# - Model does NOT generate <think> start token
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# - Model only generates </think> end token
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# - All content before </think> is reasoning
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# - All content after </think> is the actual response (content)
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# =============================================================================
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# Case: reasoning + end token + content (typical case)
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SIMPLE_REASONING = {
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"output": "This is a reasoning section</think>This is the rest",
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"reasoning": "This is a reasoning section",
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"content": "This is the rest",
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"is_reasoning_end": True,
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}
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# Case: reasoning + end token only (no content after)
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COMPLETE_REASONING = {
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"output": "This is a reasoning section</think>",
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"reasoning": "This is a reasoning section",
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"content": None,
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"is_reasoning_end": True,
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}
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# Case: no end token yet (streaming in progress, all is reasoning)
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NO_END_TOKEN = {
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"output": "This is reasoning in progress",
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"reasoning": "This is reasoning in progress",
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"content": None,
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"is_reasoning_end": False,
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}
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# Case: multiple lines of reasoning
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MULTIPLE_LINES = {
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"output": "First line\nSecond line</think>Response first line\nResponse second",
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"reasoning": "First line\nSecond line",
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"content": "Response first line\nResponse second",
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"is_reasoning_end": True,
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}
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# Case: only end token (empty reasoning, immediate response)
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SHORTEST_REASONING_NO_STREAMING = {
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"output": "</think>This is the response",
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"reasoning": "",
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"content": "This is the response",
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"is_reasoning_end": True,
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}
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# Case: only end token streaming (reasoning is None because it's just the token)
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SHORTEST_REASONING_STREAMING = {
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"output": "</think>This is the response",
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"reasoning": None,
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"content": "This is the response",
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"is_reasoning_end": True,
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}
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# Case: empty output
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EMPTY = {
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"output": "",
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"reasoning": "",
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"content": None,
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"is_reasoning_end": False,
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}
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# Case: empty streaming
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EMPTY_STREAMING = {
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"output": "",
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"reasoning": None,
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"content": None,
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"is_reasoning_end": False,
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}
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# Case: long reasoning with special characters
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SPECIAL_CHARS = {
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"output": "Let me think... 1+1=2, right?</think>Yes, 1+1=2.",
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"reasoning": "Let me think... 1+1=2, right?",
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"content": "Yes, 1+1=2.",
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"is_reasoning_end": True,
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}
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# Case: reasoning with code blocks
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CODE_IN_REASONING = {
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"output": "```python\nprint('hello')\n```</think>Here is the code.",
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"reasoning": "```python\nprint('hello')\n```",
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"content": "Here is the code.",
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"is_reasoning_end": True,
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}
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TEST_CASES = [
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# Core cases: no start token (MiniMax M2 actual behavior)
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pytest.param(
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False,
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SIMPLE_REASONING,
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id="simple_reasoning",
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),
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pytest.param(
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True,
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SIMPLE_REASONING,
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id="simple_reasoning_streaming",
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),
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pytest.param(
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False,
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COMPLETE_REASONING,
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id="complete_reasoning",
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),
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pytest.param(
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True,
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COMPLETE_REASONING,
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id="complete_reasoning_streaming",
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),
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pytest.param(
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False,
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NO_END_TOKEN,
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id="no_end_token",
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),
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pytest.param(
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True,
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NO_END_TOKEN,
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id="no_end_token_streaming",
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),
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pytest.param(
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False,
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MULTIPLE_LINES,
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id="multiple_lines",
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),
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pytest.param(
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True,
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MULTIPLE_LINES,
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id="multiple_lines_streaming",
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),
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pytest.param(
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False,
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SHORTEST_REASONING_NO_STREAMING,
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id="shortest_reasoning",
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),
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pytest.param(
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True,
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SHORTEST_REASONING_STREAMING,
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id="shortest_reasoning_streaming",
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),
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pytest.param(
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False,
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EMPTY,
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id="empty",
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),
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pytest.param(
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True,
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EMPTY_STREAMING,
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id="empty_streaming",
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),
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pytest.param(
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False,
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SPECIAL_CHARS,
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id="special_chars",
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),
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pytest.param(
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True,
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SPECIAL_CHARS,
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id="special_chars_streaming",
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),
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pytest.param(
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False,
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CODE_IN_REASONING,
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id="code_in_reasoning",
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),
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pytest.param(
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True,
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CODE_IN_REASONING,
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id="code_in_reasoning_streaming",
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),
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]
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@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
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def test_reasoning(
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streaming: bool,
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param_dict: dict,
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minimax_m2_tokenizer,
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):
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output = minimax_m2_tokenizer.tokenize(param_dict["output"])
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# decode everything to tokens
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output_tokens: list[str] = [
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minimax_m2_tokenizer.convert_tokens_to_string([token]) for token in output
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]
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parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
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minimax_m2_tokenizer
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)
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reasoning, content = run_reasoning_extraction(
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parser, output_tokens, streaming=streaming
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)
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assert reasoning == param_dict["reasoning"]
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assert content == param_dict["content"]
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# Test is_reasoning_end
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output_ids = minimax_m2_tokenizer.convert_tokens_to_ids(output)
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is_reasoning_end = parser.is_reasoning_end(output_ids)
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assert is_reasoning_end == param_dict["is_reasoning_end"]
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# Test extract_content
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if param_dict["content"] is not None:
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content = parser.extract_content_ids(output_ids)
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assert content == minimax_m2_tokenizer.convert_tokens_to_ids(
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minimax_m2_tokenizer.tokenize(param_dict["content"])
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)
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else:
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content = parser.extract_content_ids(output)
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assert content == []
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@ -19,6 +19,10 @@ logger = init_logger(__name__)
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class MiniMaxM2ReasoningParser(BaseThinkingReasoningParser):
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"""
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Reasoning parser for MiniMax M2 model.
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MiniMax M2 models don't generate <think> start token, only </think> end
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token. All content before </think> is reasoning, content after is the
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actual response.
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"""
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@property
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@ -31,6 +35,45 @@ class MiniMaxM2ReasoningParser(BaseThinkingReasoningParser):
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"""The token that ends reasoning content."""
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return "</think>"
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def extract_reasoning_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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) -> DeltaMessage | None:
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"""
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Extract reasoning content from a delta message for streaming.
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MiniMax M2 models don't generate <think> start token, so we assume
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all content is reasoning until we encounter the </think> end token.
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"""
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# Skip single end token
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if len(delta_token_ids) == 1 and delta_token_ids[0] == self.end_token_id:
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return None
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# Check if end token has already appeared in previous tokens
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# meaning we're past the reasoning phase
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if self.end_token_id in previous_token_ids:
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# We're past the reasoning phase, this is content
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return DeltaMessage(content=delta_text)
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# Check if end token is in delta tokens
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if self.end_token_id in delta_token_ids:
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# End token in delta, split reasoning and content
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end_index = delta_text.find(self.end_token)
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reasoning = delta_text[:end_index]
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content = delta_text[end_index + len(self.end_token) :]
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return DeltaMessage(
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reasoning=reasoning if reasoning else None,
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content=content if content else None,
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)
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# No end token yet, all content is reasoning
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return DeltaMessage(reasoning=delta_text)
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class MiniMaxM2AppendThinkReasoningParser(ReasoningParser):
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"""
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