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[Bugfix] Fix and add tests for GptOss reasoning parser (#28000)
Signed-off-by: Benjamin Chislett <bchislett@nvidia.com>
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tests/reasoning/test_gptoss_reasoning_parser.py
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127
tests/reasoning/test_gptoss_reasoning_parser.py
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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 vllm.reasoning import ReasoningParser
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from vllm.reasoning.gptoss_reasoning_parser import GptOssReasoningParser
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REASONING_MODEL_NAME = "openai/gpt-oss-120b"
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@pytest.fixture(scope="module")
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def gpt_oss_tokenizer():
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return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
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USER_MESSAGE_START = "<|start|>user<|message|>"
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REASONING_SECTION_START = "<|end|><|start|>assistant<|channel|>analysis<|message|>"
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ASSISTANT_CONTENT_START_PREFIX = "<|end|><|start|>assistant<|channel|>final"
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ASSISTANT_CONTENT_START_SUFFIX = "<|message|>"
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ASSISTANT_CONTENT_START = (
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ASSISTANT_CONTENT_START_PREFIX + ASSISTANT_CONTENT_START_SUFFIX
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)
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BASIC_CONTENT = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START
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+ "This is the rest",
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"is_reasoning_end": True,
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}
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BASIC_REASONING_ONLY = {
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"output": REASONING_SECTION_START + "This is reasoning" + "<|end|>",
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"is_reasoning_end": False,
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}
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BASIC_NO_REASONING_NO_ASSISTANT = {
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"output": USER_MESSAGE_START + "This is a user message",
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"is_reasoning_end": False,
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}
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# Edge-case where the model omits the assistant tag entirely.
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BASIC_NO_REASONING_ASSISTANT = {
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"output": USER_MESSAGE_START + "This is a user message<|end|><|channel|>final",
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"is_reasoning_end": True,
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}
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COMPLEX_CONTENT_INCOMPLETE_PREFIX_ONLY = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START_PREFIX,
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"is_reasoning_end": False,
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}
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COMPLEX_CONTENT_SUFFIX_ONLY = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START_SUFFIX,
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"is_reasoning_end": False,
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}
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COMPLEX_CONTENT_1_NO_SUFFIX = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START_PREFIX
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+ "<|constrain|> JSON ",
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"is_reasoning_end": False,
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}
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COMPLEX_CONTENT_1 = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START_PREFIX
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+ "<|constrain|> JSON "
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+ ASSISTANT_CONTENT_START_SUFFIX,
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"is_reasoning_end": True,
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}
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COMPLEX_CONTENT_1_WITH_CONTENT = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START_PREFIX
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+ "<|constrain|> JSON "
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+ ASSISTANT_CONTENT_START_SUFFIX
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+ "This is the rest",
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"is_reasoning_end": True,
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}
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COMPLEX_CONTENT_2 = {
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"output": REASONING_SECTION_START
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+ "This is reasoning"
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+ ASSISTANT_CONTENT_START_PREFIX
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+ "<|constrain|>ReplyAction "
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+ ASSISTANT_CONTENT_START_SUFFIX
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+ "This is the rest",
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"is_reasoning_end": True,
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}
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TEST_CASES = [
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BASIC_CONTENT,
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BASIC_REASONING_ONLY,
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COMPLEX_CONTENT_INCOMPLETE_PREFIX_ONLY,
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COMPLEX_CONTENT_SUFFIX_ONLY,
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COMPLEX_CONTENT_1_NO_SUFFIX,
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COMPLEX_CONTENT_1,
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COMPLEX_CONTENT_1_WITH_CONTENT,
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COMPLEX_CONTENT_2,
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]
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@pytest.mark.parametrize(
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"output, is_reasoning_end",
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[(t["output"], t["is_reasoning_end"]) for t in TEST_CASES],
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)
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def test_gptoss_is_reasoning_end(
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output,
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is_reasoning_end,
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gpt_oss_tokenizer,
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):
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output = gpt_oss_tokenizer.tokenize(output)
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parser: ReasoningParser = GptOssReasoningParser(gpt_oss_tokenizer)
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# Test is_reasoning_end
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output_ids = gpt_oss_tokenizer.convert_tokens_to_ids(output)
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actual_is_reasoning_end = parser.is_reasoning_end(output_ids)
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assert is_reasoning_end == actual_is_reasoning_end
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@ -67,18 +67,35 @@ class GptOssReasoningParser(ReasoningParser):
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def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
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def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
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super().__init__(tokenizer, *args, **kwargs)
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super().__init__(tokenizer, *args, **kwargs)
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self.reasoning_end_token_ids = self.model_tokenizer.encode(
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# The model can output some special tokens between "final" and "<|message|>"
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"<|start|>assistant<|channel|>final<|message|>"
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# So we need to look for both sequences to determine the end of reasoning.
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self.reasoning_end_token_ids_prefix = self.model_tokenizer.encode(
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"<|channel|>final"
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)
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)
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self.reasoning_end_token_ids_suffix = self.model_tokenizer.encode("<|message|>")
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self.reasoning_max_num_between_tokens = 20
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def is_reasoning_end(self, input_ids: list[int]) -> bool:
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def is_reasoning_end(self, input_ids: list[int]) -> bool:
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end_token_ids = self.reasoning_end_token_ids
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end_token_ids_prefix = self.reasoning_end_token_ids_prefix
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assert len(end_token_ids) > 0, "reasoning_end_token_ids is empty"
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end_token_ids_suffix = self.reasoning_end_token_ids_suffix
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assert len(end_token_ids_prefix) > 0, "reasoning_end_token_ids_prefix is empty"
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assert len(end_token_ids_suffix) > 0, "reasoning_end_token_ids_suffix is empty"
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# Check if the end sequence is present in the input_ids.
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# Check if the end sequence is present in the input_ids.
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# We search from the end of input_ids to find the last match.
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# We search from the end of input_ids to find the last match.
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for i in range(len(input_ids) - len(end_token_ids), -1, -1):
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for i in range(len(input_ids) - len(end_token_ids_prefix), -1, -1):
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if input_ids[i : i + len(end_token_ids)] == end_token_ids:
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if input_ids[i : i + len(end_token_ids_prefix)] == end_token_ids_prefix:
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return True
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# We have found the prefix, now we look for the suffix after the prefix.
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suffix_start = i + len(end_token_ids_prefix)
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for j in range(
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suffix_start, len(input_ids) - len(end_token_ids_suffix) + 1
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):
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if j - suffix_start >= self.reasoning_max_num_between_tokens:
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break
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if (
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input_ids[j : j + len(end_token_ids_suffix)]
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== end_token_ids_suffix
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):
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return True
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return False
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return False
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def extract_content_ids(self, input_ids: list[int]) -> list[int]:
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def extract_content_ids(self, input_ids: list[int]) -> list[int]:
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