"""Unit tests for srt/parser/reasoning_parser.py"""
import unittest
from sglang.srt.parser.reasoning_parser import (
Apertus2509Detector,
BaseReasoningFormatDetector,
CohereCommand4Detector,
DeepSeekR1Detector,
DeepSeekV4Detector,
Gemma4Detector,
Glm45Detector,
HunyuanDetector,
InklingDetector,
KimiDetector,
KimiK2Detector,
Nemotron3Detector,
Qwen3Detector,
ReasoningParser,
)
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
register_cpu_ci(est_time=7, suite="base-a-test-cpu")
class TestBaseReasoningFormatDetector(CustomTestCase):
def setUp(self):
self.detector = BaseReasoningFormatDetector(
think_start_token="",
think_end_token="",
force_reasoning=False,
stream_reasoning=True,
)
def test_detect_and_parse_normal_text(self):
"""Test parsing normal text without reasoning."""
text = "This is normal text"
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
def test_detect_and_parse_with_start_token(self):
"""Test parsing text starting with think token."""
text = "This is reasoning"
result = self.detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "This is reasoning")
self.assertEqual(result.normal_text, "")
def test_detect_and_parse_complete_reasoning(self):
"""Test parsing complete reasoning block."""
text = "This is reasoningThis is normal"
result = self.detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "This is reasoning")
self.assertEqual(result.normal_text, "This is normal")
def test_detect_and_parse_force_reasoning(self):
"""Test forced reasoning mode."""
detector = BaseReasoningFormatDetector(
"", "", force_reasoning=True
)
text = "This should be reasoning"
result = detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "This should be reasoning")
self.assertEqual(result.normal_text, "")
def test_parse_streaming_increment_normal(self):
"""Test streaming parse of normal text."""
result = self.detector.parse_streaming_increment("Hello world")
self.assertEqual(result.normal_text, "Hello world")
self.assertEqual(result.reasoning_text, "")
def test_parse_streaming_increment_partial_token(self):
"""Test streaming parse with partial token."""
# Test partial start token
result = self.detector.parse_streaming_increment("", "")
detector._in_reasoning = True
result = detector.parse_streaming_increment("")
self.assertEqual(result.normal_text, "")
self.assertEqual(result.reasoning_text, "")
self.assertTrue(self.detector._in_reasoning)
self.assertTrue(self.detector.stripped_think_start)
def test_parse_streaming_increment_reasoning_content(self):
"""Test streaming parse of reasoning content."""
# First add start token
self.detector.parse_streaming_increment("")
# Then add reasoning content
result = self.detector.parse_streaming_increment("reasoning content")
self.assertEqual(result.reasoning_text, "reasoning content")
self.assertEqual(result.normal_text, "")
def test_parse_streaming_increment_end_token(self):
"""Test streaming parse with end token."""
# Start reasoning mode
self.detector.parse_streaming_increment("")
self.detector.parse_streaming_increment("reasoning")
# End reasoning - the reasoning content accumulated in previous calls is cleared when end token is found
result = self.detector.parse_streaming_increment("normal text")
self.assertEqual(result.reasoning_text, "") # Buffer cleared, returns empty
self.assertEqual(result.normal_text, "normal text")
self.assertFalse(self.detector._in_reasoning)
def test_parse_streaming_increment_no_stream_reasoning(self):
"""Test streaming parse without streaming reasoning."""
detector = BaseReasoningFormatDetector(
"", "", stream_reasoning=False
)
# Start reasoning mode
detector.parse_streaming_increment("")
# Add reasoning content - should not return content
result = detector.parse_streaming_increment("reasoning content")
self.assertEqual(result.reasoning_text, "")
self.assertEqual(result.normal_text, "")
def test_parse_streaming_increment_mixed_content(self):
"""Test streaming parse with mixed content in one chunk."""
result = self.detector.parse_streaming_increment(
"reasoningnormal"
)
self.assertEqual(result.reasoning_text, "reasoning")
self.assertEqual(result.normal_text, "normal")
def test_finish_flushes_truncated_reasoning_no_stream_reasoning(self):
"""Bug regression: with stream_reasoning=False the base detector buffers
the whole thinking block and only emits it on . A stream cut
short (e.g. max_tokens) before left the trace stuck in _buffer,
and finish() dropped it. finish() must now emit it as reasoning, with the
opening think token stripped, matching the non-streaming path."""
detector = BaseReasoningFormatDetector(
"", "", stream_reasoning=False
)
detector.parse_streaming_increment("")
self.assertEqual(
detector.parse_streaming_increment("half a thought").reasoning_text, ""
)
end = detector.finish()
self.assertEqual(end.reasoning_text, "half a thought")
self.assertEqual(end.normal_text, "")
# State is cleared, so a second finish() is a no-op (no duplicate flush).
self.assertEqual(detector._buffer, "")
self.assertEqual(detector.finish().reasoning_text, "")
def test_finish_flushes_partial_end_tag_when_streaming_reasoning(self):
"""With stream_reasoning=True everything in _buffer at the end of the
stream is a trailing slice that was held back precisely because it could
still have grown into ``, so it was never emitted. Since the
stream ended it never became a token, and dropping it would lose content
whose only crime is looking like the start of one -- reasoning ending in
a literal `<` is the common case. Flushing matches stream_reasoning=False
and the non-streaming path, which both keep it."""
detector = BaseReasoningFormatDetector(
"", "", stream_reasoning=True
)
self.assertEqual(
detector.parse_streaming_increment("thought").reasoning_text,
"thought",
)
self.assertEqual(detector.parse_streaming_increment(" token."""
text = "I need to think about this.The answer is 42."
result = self.detector.detect_and_parse(text)
# Should be treated as reasoning because force_reasoning=True
self.assertEqual(result.reasoning_text, "I need to think about this.")
self.assertEqual(result.normal_text, "The answer is 42.")
def test_finish_flushes_truncated_forced_reasoning(self):
"""Bug regression: DeepSeek-R1 forces reasoning without a start
token, so the whole output is reasoning until . With
stream_reasoning=False a stream cut before buffered the trace;
finish() must flush it as reasoning instead of dropping it."""
detector = DeepSeekR1Detector(stream_reasoning=False)
self.assertEqual(
detector.parse_streaming_increment(
"reasoning with no end token"
).reasoning_text,
"",
)
end = detector.finish()
self.assertEqual(end.reasoning_text, "reasoning with no end token")
self.assertEqual(end.normal_text, "")
class TestQwen3Detector(CustomTestCase):
def setUp(self):
self.detector = Qwen3Detector()
def test_detect_and_parse_without_thinking(self):
"""Test parsing without thinking (enable_thinking=False case)."""
text = "Direct answer without thinking."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
class TestDeepSeekV4Detector(CustomTestCase):
def test_strict_thinking_excludes_deepseek_control_tokens(self):
detector = ReasoningParser(model_type="deepseek-v4").detector
self.assertIsInstance(detector, DeepSeekV4Detector)
self.assertEqual(
detector.think_excluded_tokens,
["<｜end▁of▁sentence｜>", "｜DSML｜"],
)
def test_thinking_stays_explicit_opt_in(self):
detector = ReasoningParser(model_type="deepseek-v4").detector
self.assertEqual(detector.reasoning_default, "explicit_thinking")
self.assertTrue(detector.thinks_internally)
def test_dsml_block_is_routed_out_of_reasoning(self):
"""Without tool_start_token the DSML block stays in reasoning_content and
the tool call detector never sees it."""
detector = ReasoningParser(model_type="deepseek-v4").detector
self.assertEqual(detector.tool_start_token, "<｜DSML｜")
result = detector.parse_streaming_increment(
'pick a tool<｜DSML｜tool_calls><｜DSML｜invoke name="s">'
)
self.assertEqual(result.reasoning_text, "pick a tool")
self.assertTrue(result.normal_text.startswith("<｜DSML｜tool_calls>"))
class TestInklingDetector(CustomTestCase):
def test_streaming_routes_blocks_across_all_string_boundaries(self):
detector = InklingDetector()
source = (
"<|message_model|><|content_thinking|>think<|end_message|>"
"<|message_model|><|content_text|>answer<|end_message|>"
"<|content_model_end_sampling|>"
)
reasoning = ""
content = ""
for char in source:
result = detector.parse_streaming_increment(char)
reasoning += result.reasoning_text
content += result.normal_text
self.assertEqual(reasoning, "think")
self.assertEqual(content, "answer")
def test_tool_header_is_preserved_for_the_tool_parser(self):
detector = InklingDetector()
source = (
"<|message_model|>weather<|content_invoke_tool_json|>"
'{"name":"weather","args":{"city":"SF"}}<|end_message|>'
)
content = ""
for char in source:
content += detector.parse_streaming_increment(char).normal_text
self.assertEqual(content, source)
def test_raw_text_tool_framing_is_preserved_for_the_tool_parser(self):
"""The headerless <|content_invoke_tool_text|> block must survive into
content so the tool-call detector can surface it, rather than being
swallowed as header data."""
detector = InklingDetector()
source = "<|message_model|><|content_invoke_tool_text|>search<|end_message|>"
result = detector.detect_and_parse(source)
self.assertIn("<|content_invoke_tool_text|>", result.normal_text)
self.assertIn("search", result.normal_text)
self.assertEqual(result.reasoning_text, "")
def test_quoted_message_model_token_inside_content_is_preserved(self):
"""Bug regression: the header branch flipped to header state on ANY
<|message_model|> occurrence, so a literal token the model wrote
inside a content block (e.g. quoting the protocol) silently swallowed
all payload text up to the next control token."""
detector = InklingDetector()
source = (
"<|message_model|><|content_text|>Header token: <|message_model|>"
" then more text<|end_message|>"
)
result = detector.detect_and_parse(source)
self.assertEqual(
result.normal_text, "Header token: <|message_model|> then more text"
)
def test_control_token_inside_tool_header_shares_the_full_alphabet(self):
"""Bug regression: the tool-call detector validated headers against
INKLING_SPECIAL_TOKENS while the reasoning parser keyed on the larger
control alphabet (+ <|model_trigger_generation|>), so a control token
smuggled inside a header passed one machine and not the other."""
from sglang.srt.function_call.inkling_detector import (
InklingDetector as ToolDetector,
)
detector = ToolDetector()
prefix, name = detector._split_trailing_tool_header(
"<|message_model|>weather<|model_trigger_generation|>"
)
self.assertIsNone(name)
def test_continuation_stream_text_survives_chunk_boundaries(self):
"""Bug regression: text arriving with no open block (a
continue_final_message stream resumes MID text block) was routed to
content only when a chunk held no control token; a chunk like
'ld<|end_message|>' silently dropped the 'ld'. All out-of-block text
must reach content regardless of chunking."""
source = (
" world<|end_message|><|message_model|><|content_text|>next<|end_message|>"
)
for chunks in (
[source],
[
" wor",
"ld<|end_message|>",
"<|message_model|><|content_text|>next<|end_message|>",
],
list(source),
):
detector = InklingDetector()
content = ""
for chunk in chunks:
content += detector.parse_streaming_increment(chunk).normal_text
self.assertEqual(content, " worldnext", msg=f"chunks={chunks!r}")
def test_finish_flushes_reasoning_truncated_before_end_token(self):
"""Bug regression: with stream_reasoning=False the detector buffers the
thinking block and only flushes it on a control/end token. When
generation is cut mid-block (e.g. max_tokens) the stream ends with no
end token, so the buffered trace was dropped entirely; finish() must
emit it, matching the non-streaming detect_and_parse path."""
detector = InklingDetector(stream_reasoning=False)
source = "<|message_model|><|content_thinking|>truncated thinking"
streamed_reasoning = ""
for char in source:
streamed_reasoning += detector.parse_streaming_increment(
char
).reasoning_text
# The block never closed, so nothing surfaces mid-stream.
self.assertEqual(streamed_reasoning, "")
# finish() flushes the buffered trace instead of dropping it.
self.assertEqual(detector.finish().reasoning_text, "truncated thinking")
class TestKimiDetector(CustomTestCase):
def setUp(self):
self.detector = KimiDetector()
def test_detect_and_parse_kimi_no_thinking(self):
"""Test parsing Kimi format without thinking."""
text = "Direct answer without thinking tokens."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
class TestKimiK2Detector(CustomTestCase):
"""Test cases for KimiK2 detector with tool interruption support."""
def setUp(self):
self.detector = KimiK2Detector()
def test_init(self):
"""Test KimiK2Detector initialization."""
self.assertEqual(self.detector.think_start_token, "")
self.assertEqual(self.detector.think_end_token, "")
self.assertEqual(self.detector.tool_start_token, "<|tool_calls_section_begin|>")
self.assertFalse(self.detector._in_reasoning)
self.assertTrue(self.detector.stream_reasoning)
class TestGlm45Detector(CustomTestCase):
"""Test cases for GLM45 detector with tool interruption support."""
def setUp(self):
self.detector = Glm45Detector()
def test_detect_and_parse_multiple_tool_calls_find(self):
"""
Test that find() finds the FIRST occurrence of tool_start_token.
If multiple tool calls exist in buffer, should split at the first one.
"""
text = "thinkingfirst toolsecond toolfinal tool"
result = self.detector.detect_and_parse(text)
# Should split at the first
self.assertEqual(result.reasoning_text, "thinking")
self.assertEqual(
result.normal_text,
"first toolsecond toolfinal tool",
)
def test_detect_and_parse_normal_text_only(self):
"""Test parsing text without reasoning block."""
text = "Just the answer without any reasoning."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
def test_streaming_no_stream_reasoning(self):
"""Test streaming without stream_reasoning enabled."""
detector = Glm45Detector(stream_reasoning=False)
# Start reasoning
detector.parse_streaming_increment("")
# Reasoning content is buffered and not returned yet
result = detector.parse_streaming_increment("thinking")
self.assertEqual(result.reasoning_text, "")
self.assertEqual(result.normal_text, "")
# Tool interruption should still work - flushes buffered reasoning. The
# opening tag is stripped from `self._buffer` as well as from the local
# view, so the flush matches detect_and_parse instead of carrying the raw
# tag into reasoning_content.
result = detector.parse_streaming_increment("tool call")
self.assertEqual(result.reasoning_text, "thinking")
self.assertEqual(result.normal_text, "tool call")
def test_streaming_empty_reasoning_with_tool(self):
"""Test empty reasoning block followed by tool call."""
result1 = self.detector.parse_streaming_increment("")
result2 = self.detector.parse_streaming_increment("tool call")
self.assertEqual(result2.reasoning_text, "")
self.assertEqual(result2.normal_text, "tool call")
def test_forced_reasoning_mode(self):
"""Test GLM45 with force_reasoning=True."""
detector = Glm45Detector(force_reasoning=True)
# Without start token, should still be in reasoning mode
text = "This is reasoning"
result = detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "This is reasoning")
self.assertEqual(result.normal_text, "")
# Tool interruption should work with forced reasoning
text = "More reasoningtool call"
result = detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "More reasoning")
self.assertEqual(result.normal_text, "tool call")
class TestHunyuanDetector(CustomTestCase):
"""Test cases for Hunyuan detector with tool interruption support."""
def setUp(self):
self.detector = HunyuanDetector()
def test_detect_and_parse_without_thinking(self):
"""Test parsing without thinking tokens (no_think mode)."""
text = "Direct answer without thinking."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
def test_reasoning_parser_integration(self):
"""Test Hunyuan through ReasoningParser API."""
parser = ReasoningParser("hunyuan")
self.assertIsInstance(parser.detector, HunyuanDetector)
# Non-streaming
reasoning, normal = parser.parse_non_stream(
"thinkingfunc"
)
self.assertEqual(reasoning, "thinking")
self.assertIn("", normal)
def test_reasoning_parser_streaming(self):
"""Test Hunyuan streaming through ReasoningParser API."""
parser = ReasoningParser("hunyuan")
chunks = ["", "reasoning", "", "func"]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
reasoning, normal = parser.parse_stream_chunk(chunk)
if reasoning:
all_reasoning += reasoning
if normal:
all_normal += normal
self.assertEqual(all_reasoning, "reasoning")
self.assertIn("", all_normal)
class TestNemotron3Detector(CustomTestCase):
def setUp(self):
self.detector = Nemotron3Detector()
def test_detect_and_parse_no_thinking(self):
"""Test parsing without thinking tokens."""
text = "Direct answer without thinking."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
def test_detect_and_parse_reasoning_only(self):
"""Test parsing when output is all reasoning (no content after )."""
text = "All reasoning, no answer"
result = self.detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "All reasoning, no answer")
self.assertEqual(result.normal_text, "")
def test_force_nonempty_content_swaps_when_no_normal_text(self):
"""Test force_nonempty_content swaps reasoning to content when content is empty."""
detector = Nemotron3Detector(force_nonempty_content=True)
text = "All reasoning, no answer"
result = detector.detect_and_parse(text)
self.assertEqual(result.normal_text, "All reasoning, no answer")
self.assertEqual(result.reasoning_text, "")
def test_force_nonempty_content_no_swap_when_normal_text_exists(self):
"""Test force_nonempty_content does not swap when content already exists."""
detector = Nemotron3Detector(force_nonempty_content=True)
text = "Reasoning hereThe answer is 42."
result = detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "Reasoning here")
self.assertEqual(result.normal_text, "The answer is 42.")
def test_force_nonempty_content_truncated_reasoning(self):
"""Test force_nonempty_content with truncated reasoning (no end token)."""
detector = Nemotron3Detector(force_nonempty_content=True)
text = "Truncated reasoning without end token"
result = detector.detect_and_parse(text)
# Truncated reasoning has no normal_text, so swap should occur
self.assertEqual(result.normal_text, "Truncated reasoning without end token")
self.assertEqual(result.reasoning_text, "")
def test_streaming_truncated_reasoning_reclassified_on_finish(self):
"""force_nonempty_content: truncated reasoning (no think_end) is flushed
as normal_text when the stream ends, so streaming content is non-empty."""
detector = Nemotron3Detector(force_nonempty_content=True)
detector.parse_streaming_increment(detector.think_start_token)
detector.parse_streaming_increment("reasoning part one")
detector.parse_streaming_increment(" more reasoning")
end = detector.finish()
self.assertEqual(end.reasoning_text, "")
self.assertEqual(end.normal_text, "reasoning part one more reasoning")
def test_streaming_tool_start_ends_reasoning_and_noops_finish(self):
"""tool_start_token interrupts reasoning; finish() then no-ops because
_in_reasoning is already False."""
detector = Nemotron3Detector(force_nonempty_content=True)
detector.parse_streaming_increment(detector.think_start_token)
detector.parse_streaming_increment("reasoning here")
result = detector.parse_streaming_increment(
detector.tool_start_token + "payload"
)
self.assertEqual(result.reasoning_text, "")
self.assertEqual(result.normal_text, detector.tool_start_token + "payload")
self.assertFalse(detector._in_reasoning)
end = detector.finish()
self.assertEqual(end.normal_text, "")
def test_streaming_truncated_no_stream_reasoning_strips_think_start(self):
"""force_nonempty_content + stream_reasoning=False: the opening think
token must not leak into content when truncation is flushed on finish.
Regression: with stream_reasoning=False the base parse_streaming_increment
never clears _buffer, so the stripped think_start survives in _buffer and
finish() would prepend it to the reclassified content."""
detector = Nemotron3Detector(
force_nonempty_content=True, stream_reasoning=False
)
detector.parse_streaming_increment(detector.think_start_token)
detector.parse_streaming_increment("hidden reasoning")
end = detector.finish()
self.assertEqual(end.reasoning_text, "")
self.assertEqual(end.normal_text, "hidden reasoning")
self.assertNotIn(detector.think_start_token, end.normal_text)
class TestApertus2509DetectorForceNonempty(CustomTestCase):
"""force_nonempty_content swap on Apertus2509 (non-streaming, via base helper)."""
def test_swap_when_only_reasoning(self):
detector = Apertus2509Detector(force_nonempty_content=True)
text = (
detector.think_start_token
+ "apertus reasoning only"
+ detector.think_end_token
)
result = detector.detect_and_parse(text)
self.assertEqual(result.normal_text, "apertus reasoning only")
self.assertEqual(result.reasoning_text, "")
def test_no_swap_when_normal_exists(self):
detector = Apertus2509Detector(force_nonempty_content=True)
text = (
detector.think_start_token + "reason" + detector.think_end_token + "answer"
)
result = detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "reason")
self.assertEqual(result.normal_text, "answer")
class TestGemma4Detector(CustomTestCase):
def setUp(self):
self.detector = Gemma4Detector()
def test_detect_and_parse_complete_reasoning(self):
"""Test parsing complete Gemma4 reasoning block (think_start_self_label is stripped)."""
text = "<|channel>thought\nLet me think about thisThe answer is 42."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.reasoning_text, "Let me think about this")
self.assertEqual(result.normal_text, "The answer is 42.")
def test_detect_and_parse_without_thinking(self):
"""Test parsing without thinking (enable_thinking=False case)."""
text = "Direct answer without thinking."
result = self.detector.detect_and_parse(text)
self.assertEqual(result.normal_text, text)
self.assertEqual(result.reasoning_text, "")
def test_streaming_end_token_mid_chunk(self):
"""Test end token arriving in the same chunk as reasoning content."""
self.detector.parse_streaming_increment("<|channel>thought\n")
result = self.detector.parse_streaming_increment(
"some reasoningthe answer"
)
self.assertEqual(result.reasoning_text, "some reasoning")
self.assertEqual(result.normal_text, "the answer")
self.assertFalse(self.detector._in_reasoning)
def test_streaming_self_label_split_across_chunks(self):
"""Test self_label ('thought\\n') arriving separately from start token."""
result1 = self.detector.parse_streaming_increment("<|channel>")
self.assertEqual(result1.reasoning_text, "")
self.assertEqual(result1.normal_text, "")
result2 = self.detector.parse_streaming_increment("thought\n")
self.assertTrue(self.detector._in_reasoning)
result3 = self.detector.parse_streaming_increment("reasoning here")
self.assertEqual(result3.reasoning_text, "reasoning here")
class TestReasoningParser(CustomTestCase):
def test_init_valid_model(self):
"""Test initialization with valid model types."""
parser = ReasoningParser("deepseek-r1")
self.assertIsInstance(parser.detector, DeepSeekR1Detector)
parser = ReasoningParser("qwen3")
self.assertIsInstance(parser.detector, Qwen3Detector)
parser = ReasoningParser("kimi")
self.assertIsInstance(parser.detector, KimiDetector)
parser = ReasoningParser("kimi_k2")
self.assertIsInstance(parser.detector, KimiK2Detector)
parser = ReasoningParser("glm45")
self.assertIsInstance(parser.detector, Glm45Detector)
parser = ReasoningParser("hunyuan")
self.assertIsInstance(parser.detector, HunyuanDetector)
parser = ReasoningParser("gemma4")
self.assertIsInstance(parser.detector, Gemma4Detector)
def test_init_invalid_model(self):
"""Test initialization with invalid model type."""
with self.assertRaises(ValueError) as context:
ReasoningParser("invalid-model")
self.assertIn("Unsupported model type", str(context.exception))
def test_init_no_model(self):
"""Test initialization without model type."""
with self.assertRaises(ValueError) as context:
ReasoningParser(None)
self.assertEqual(str(context.exception), "Model type must be specified")
def test_parse_non_stream(self):
"""Test non-streaming parsing."""
parser = ReasoningParser("qwen3")
reasoning, normal = parser.parse_non_stream(
"Let me thinkThe answer is 42."
)
self.assertEqual(reasoning, "Let me think")
self.assertEqual(normal, "The answer is 42.")
def test_parse_non_stream_preserves_payload_whitespace(self):
"""Non-streaming parsing must not rewrite text inside or after reasoning."""
parser = ReasoningParser("qwen3")
reasoning, normal = parser.parse_non_stream(
"\nLet me think\n\n\nThe answer is 42.\n"
)
self.assertEqual(reasoning, "\nLet me think\n")
self.assertEqual(normal, "\n\nThe answer is 42.\n")
def test_parse_non_stream_strips_repeated_leading_start_tokens(self):
"""Repeated leading start tokens are markers, not reasoning payload."""
parser = ReasoningParser("qwen3")
reasoning, normal = parser.parse_non_stream(
"Let me thinkThe answer is 42."
)
self.assertEqual(reasoning, "Let me think")
self.assertEqual(normal, "The answer is 42.")
def test_parse_stream_chunk_preserves_payload_whitespace(self):
"""Streaming parsing preserves the same generated payload whitespace."""
parser = ReasoningParser("qwen3")
reasoning, normal = parser.parse_stream_chunk("")
self.assertEqual(reasoning, "")
self.assertEqual(normal, "")
reasoning, normal = parser.parse_stream_chunk(
"\nLet me think\n\n\nThe answer is 42.\n"
)
self.assertEqual(reasoning, "\nLet me think\n")
self.assertEqual(normal, "\n\nThe answer is 42.\n")
def test_parse_stream_chunk(self):
"""Test streaming chunk parsing."""
parser = ReasoningParser("qwen3")
# First chunk with start token
reasoning, normal = parser.parse_stream_chunk("")
self.assertEqual(reasoning, "")
self.assertEqual(normal, "")
# Second chunk with reasoning content
reasoning, normal = parser.parse_stream_chunk("thinking...")
self.assertEqual(reasoning, "thinking...")
self.assertEqual(normal, "")
# Third chunk with end token and normal text
reasoning, normal = parser.parse_stream_chunk("answer")
self.assertEqual(reasoning, "") # Buffer cleared when end token processed
self.assertEqual(normal, "answer")
def test_case_insensitive_model_type(self):
"""Test case insensitive model type matching."""
parser1 = ReasoningParser("DeepSeek-R1")
parser2 = ReasoningParser("QWEN3")
parser3 = ReasoningParser("Kimi")
self.assertIsInstance(parser1.detector, DeepSeekR1Detector)
self.assertIsInstance(parser2.detector, Qwen3Detector)
self.assertIsInstance(parser3.detector, KimiDetector)
def test_stream_reasoning_parameter(self):
"""Test stream_reasoning parameter is passed correctly."""
parser = ReasoningParser("qwen3", stream_reasoning=False)
self.assertFalse(parser.detector.stream_reasoning)
parser = ReasoningParser("qwen3", stream_reasoning=True)
self.assertTrue(parser.detector.stream_reasoning)
def test_glm45_tool_interruption(self):
"""Test GLM45 tool interruption through ReasoningParser API."""
parser = ReasoningParser("glm45")
# Non-streaming: tool interrupt
reasoning, normal = parser.parse_non_stream(
"thinkingtool call"
)
self.assertEqual(reasoning, "thinking")
self.assertEqual(normal, "tool call")
# Streaming: tool interrupt
parser = ReasoningParser("glm45")
chunks = ["", "reasoning", "", "tool args"]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
reasoning, normal = parser.parse_stream_chunk(chunk)
if reasoning:
all_reasoning += reasoning
if normal:
all_normal += normal
self.assertEqual(all_reasoning, "reasoning")
self.assertEqual(all_normal, "tool args")
def test_kimik2_tool_interruption(self):
"""Test Kimi-K2 tool interruption through ReasoningParser API."""
parser = ReasoningParser("kimi_k2")
# Non-streaming: tool interrupt
reasoning, normal = parser.parse_non_stream(
"thinking<|tool_calls_section_begin|><|tool_call_begin|>"
)
self.assertEqual(reasoning, "thinking")
self.assertEqual(normal, "<|tool_calls_section_begin|><|tool_call_begin|>")
# Streaming: tool interrupt
parser = ReasoningParser("kimi_k2")
chunks = [
"",
"reasoning",
"<|tool_calls_section_begin|>",
"<|tool_call_begin|>",
]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
reasoning, normal = parser.parse_stream_chunk(chunk)
if reasoning:
all_reasoning += reasoning
if normal:
all_normal += normal
self.assertEqual(all_reasoning, "reasoning")
self.assertEqual(all_normal, "<|tool_calls_section_begin|><|tool_call_begin|>")
class TestIntegrationScenarios(CustomTestCase):
"""Integration tests for realistic usage scenarios."""
def test_kimi_streaming_scenario(self):
"""Test Kimi streaming scenario."""
parser = ReasoningParser("kimi")
chunks = [
"◁thi",
"nk▷",
"Let me analyze this problem.",
" I need to consider multiple factors.",
"◁/th",
"ink▷",
"The answer is 42.",
]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
reasoning, normal = parser.parse_stream_chunk(chunk)
all_reasoning += reasoning
all_normal += normal
self.assertIn("analyze", all_reasoning)
self.assertIn("multiple factors", all_reasoning)
self.assertIn("42", all_normal)
def test_empty_reasoning_blocks(self):
"""Test handling of empty reasoning blocks."""
parser = ReasoningParser("qwen3")
text = "Just the answer."
reasoning, normal = parser.parse_non_stream(text)
self.assertEqual(reasoning, "")
self.assertEqual(normal, "Just the answer.")
def test_qwen3_forced_reasoning_complete_response(self):
"""Test complete Qwen3-ForcedReasoning response parsing."""
parser = ReasoningParser("qwen3", force_reasoning=True)
text = "Let me solve this step by step. The equation is x + 2 = 5. Subtracting 2 from both sides gives x = 3.The solution is x = 3."
reasoning, normal = parser.parse_non_stream(text)
self.assertIn("step by step", reasoning)
self.assertIn("x = 3", reasoning)
self.assertEqual(normal, "The solution is x = 3.")
def test_qwen3_forced_reasoning_streaming_scenario(self):
"""Test Qwen3-ForcedReasoning streaming scenario."""
parser = ReasoningParser("qwen3", force_reasoning=True)
chunks = [
"I need to analyze",
" this problem carefully.",
" Let me break it down.",
"",
"The final answer is 42.",
]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
reasoning, normal = parser.parse_stream_chunk(chunk)
all_reasoning += reasoning
all_normal += normal
self.assertIn("analyze", all_reasoning)
self.assertIn("break it down", all_reasoning)
self.assertIn("final answer", all_normal)
class TestBufferLossBugFix(CustomTestCase):
"""Test cases for the buffer loss bug fix in parse_streaming_increment."""
def test_partial_end_tag_buffer_loss_bug(self):
"""
Test the bug where partial end tag fragments are lost when followed by normal text.
Bug scenario:
1. _in_reasoning is False
2. new_text is "", "")
# Step 1: Send partial end tag when not in reasoning mode
# This should be buffered since it could be start of ""
result1 = detector.parse_streaming_increment("", "")
# Enter reasoning mode
detector.parse_streaming_increment("")
detector.parse_streaming_increment("some reasoning")
# Send partial end tag
result1 = detector.parse_streaming_increment("normal text")
self.assertEqual(result2.normal_text, "normal text")
# The reasoning text should be empty since buffer was cleared when end tag was processed
self.assertEqual(result2.reasoning_text, "")
def test_edge_case_exact_token_match(self):
"""
Test edge case where buffer content exactly matches a token.
"""
detector = BaseReasoningFormatDetector("", "")
# Build up the exact start token character by character
detector.parse_streaming_increment("<")
detector.parse_streaming_increment("t")
detector.parse_streaming_increment("h")
detector.parse_streaming_increment("i")
detector.parse_streaming_increment("n")
result = detector.parse_streaming_increment("k>")
# Should enter reasoning mode
self.assertEqual(result.normal_text, "")
self.assertEqual(result.reasoning_text, "")
self.assertTrue(detector._in_reasoning)
self.assertTrue(detector.stripped_think_start)
class TestStreamingChunkSizeInvariance(CustomTestCase):
"""Accumulated (reasoning, normal) output must not depend on how the decode
steps happen to batch tokens, and must match one-shot detect_and_parse.
Speculative decoding and stream_interval > 1 deliver multiple tokens per
step, which splits multi-character tokens like `` across chunk
boundaries. The two `_is_chunk_dependent` tests pin known exceptions.
"""
CHUNK_SIZES = [1, 2, 3, 5, 7, 11, 23, 1000]
DSML = "｜DSML｜"
def _feed(self, detector, text, chunk_size):
reasoning = normal = ""
for i in range(0, len(text), chunk_size):
result = detector.parse_streaming_increment(text[i : i + chunk_size])
reasoning += result.reasoning_text
normal += result.normal_text
result = detector.finish()
return reasoning + result.reasoning_text, normal + result.normal_text
def _assert_invariant(self, make_detector, text, expected):
for chunk_size in self.CHUNK_SIZES:
with self.subTest(chunk_size=chunk_size):
self.assertEqual(
self._feed(make_detector(), text, chunk_size), expected
)
one_shot = make_detector().detect_and_parse(text)
self.assertEqual((one_shot.reasoning_text, one_shot.normal_text), expected)
def test_think_end_split_across_chunks(self):
"""`` straddling a chunk boundary must still end the block."""
self._assert_invariant(
DeepSeekR1Detector,
"abc reasoningnormal text",
("abc reasoning", "normal text"),
)
def test_think_end_split_buffered_mode(self):
self._assert_invariant(
lambda: DeepSeekR1Detector(stream_reasoning=False),
"abc reasoningnormal text",
("abc reasoning", "normal text"),
)
def test_literal_angle_bracket_in_reasoning_is_not_swallowed(self):
self._assert_invariant(
DeepSeekR1Detector,
"a < btail",
("a < b", "tail"),
)
def test_reasoning_truncated_mid_partial_token(self):
"""Reasoning that happens to end in a `` prefix must keep those
characters: the holdback exists to recombine them with the next chunk, so
a stream that ends first must flush rather than swallow them."""
for chunk_size in self.CHUNK_SIZES:
with self.subTest(chunk_size=chunk_size):
self.assertEqual(
self._feed(DeepSeekR1Detector(), "compare a <", chunk_size),
("compare a <", ""),
)
def test_normal_text_ending_in_token_prefix_survives(self):
"""Content after the reasoning block that happens to end in a ``
prefix is buffered by the prefix check; the stream ending must flush it."""
for text in ("ab<", "ab", 1)[0].removeprefix(""),
text.split("", 1)[1],
)
for chunk_size in self.CHUNK_SIZES:
with self.subTest(text=text, chunk_size=chunk_size):
self.assertEqual(
self._feed(DeepSeekR1Detector(), text, chunk_size), expected
)
def test_text_before_think_token_is_chunk_dependent(self):
"""Accepted divergence, inherited from main: text before `` lands
in reasoning or content depending on where the chunk boundary falls."""
text = "leadrtail"
variants = {
self._feed(Qwen3Detector(), text, chunk_size)
for chunk_size in self.CHUNK_SIZES
}
self.assertEqual(
variants,
{("r", "leadtail"), ("", text), ("leadr", "tail")},
)
# And the non-streaming path produces yet a fourth split.
one_shot = Qwen3Detector().detect_and_parse(text)
self.assertEqual(
(one_shot.reasoning_text, one_shot.normal_text), ("leadr", "tail")
)
def test_dsv4_reasoning_quoting_dsml_is_chunk_dependent(self):
"""Accepted divergence: streaming ends the block at the DSML marker, while
one-shot waits to see whether a `` follows. Reachable because the
DSV4 system prompt shows that marker to the model."""
text = f"format is <{self.DSML}tool_calls>answer"
by_output = {}
for chunk_size in self.CHUNK_SIZES:
by_output.setdefault(
self._feed(DeepSeekV4Detector(), text, chunk_size), []
).append(chunk_size)
self.assertEqual(len(by_output), 2, f"expected two variants, got {by_output}")
early_cut = ("format is ", f"<{self.DSML}tool_calls>answer")
whole_buffer = (f"format is <{self.DSML}tool_calls>", "answer")
self.assertIn(early_cut, by_output)
self.assertIn(whole_buffer, by_output)
one_shot = DeepSeekV4Detector().detect_and_parse(text)
self.assertEqual((one_shot.reasoning_text, one_shot.normal_text), whole_buffer)
def test_dsv4_tool_block_after_think_end(self):
tool_call = (
f"<{self.DSML}tool_calls>"
f'<{self.DSML}invoke name="s">'
f""
)
self._assert_invariant(
DeepSeekV4Detector,
f"my reasoning{tool_call}",
("my reasoning", tool_call),
)
def test_dsv4_tool_block_without_think_end(self):
"""DSML directly after reasoning must still be routed to normal_text so
the tool call detector can see it."""
tool_call = (
f"<{self.DSML}tool_calls>"
f'<{self.DSML}invoke name="s">'
f""
)
for chunk_size in self.CHUNK_SIZES:
with self.subTest(chunk_size=chunk_size):
self.assertEqual(
self._feed(
DeepSeekV4Detector(),
f"my reasoning{tool_call}",
chunk_size,
),
("my reasoning", tool_call),
)
class TestGptOssDetector(CustomTestCase):
"""Test cases for GptOssDetector which delegates to HarmonyParser."""
def setUp(self):
from sglang.srt.parser.reasoning_parser import GptOssDetector
self.detector = GptOssDetector()
def test_streaming_analysis_then_final(self):
"""Test streaming parse across multiple chunks."""
chunks = [
"<|start|><|channel|>analysis<|message|>",
"reasoning part",
"<|end|>",
"<|channel|>final<|message|>answer",
"<|end|>",
]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
result = self.detector.parse_streaming_increment(chunk)
all_reasoning += result.reasoning_text
all_normal += result.normal_text
self.assertIn("reasoning part", all_reasoning)
self.assertIn("answer", all_normal)
class TestMiniMaxAppendThinkDetector(CustomTestCase):
"""Test cases for MiniMaxAppendThinkDetector."""
def setUp(self):
from sglang.srt.parser.reasoning_parser import MiniMaxAppendThinkDetector
self.detector = MiniMaxAppendThinkDetector()
def test_detect_and_parse_prepends_think(self):
"""Test that detect_and_parse prepends to the text."""
result = self.detector.detect_and_parse("Hello world")
self.assertEqual(result.normal_text, "Hello world")
def test_streaming_first_chunk_prepends_think(self):
"""Test that first streaming chunk gets prepended."""
result = self.detector.parse_streaming_increment("First chunk")
self.assertEqual(result.normal_text, "First chunk")
def test_streaming_second_chunk_no_prepend(self):
"""Test that subsequent streaming chunks are passed through."""
self.detector.parse_streaming_increment("First")
result = self.detector.parse_streaming_increment("Second")
self.assertEqual(result.normal_text, "Second")
class TestMiniMaxM3Detector(CustomTestCase):
"""Test cases for MiniMaxM3Detector multi-turn stray-closer handling."""
def _detector(self, force_reasoning=False):
from sglang.srt.parser.reasoning_parser import MiniMaxM3Detector
return MiniMaxM3Detector(force_reasoning=force_reasoning)
def test_drops_leading_stray_close_non_stream(self):
"""Non-thinking multi-turn reply opening with a stray ."""
result = self._detector().detect_and_parse("The answer is 42.")
self.assertEqual(result.normal_text, "The answer is 42.")
self.assertEqual(result.reasoning_text or "", "")
def test_drops_leading_stray_close_with_whitespace(self):
result = self._detector().detect_and_parse("\nHello")
self.assertEqual(result.normal_text, "Hello")
def test_plain_reply_untouched(self):
result = self._detector().detect_and_parse("Just a normal answer.")
self.assertEqual(result.normal_text, "Just a normal answer.")
def test_real_reasoning_block_non_stream(self):
result = self._detector().detect_and_parse(
"reasoning herefinal"
)
self.assertEqual(result.reasoning_text, "reasoning here")
self.assertEqual(result.normal_text, "final")
def test_thinking_mode_close_not_dropped(self):
"""force_reasoning=True means the closer ends reasoning, not a stray drop."""
result = self._detector(force_reasoning=True).detect_and_parse(
"reasoninganswer"
)
self.assertEqual(result.reasoning_text, "reasoning")
self.assertEqual(result.normal_text, "answer")
def test_drops_leading_stray_close_stream_single_token(self):
detector = self._detector()
first = detector.parse_streaming_increment("")
self.assertEqual(first.normal_text or "", "")
second = detector.parse_streaming_increment("The answer.")
self.assertEqual(second.normal_text, "The answer.")
def test_drops_leading_stray_close_stream_after_whitespace(self):
"""A leading whitespace token before the atomic is buffered."""
detector = self._detector()
self.assertEqual(detector.parse_streaming_increment(" ").normal_text or "", "")
self.assertEqual(
detector.parse_streaming_increment("").normal_text or "", ""
)
self.assertEqual(
detector.parse_streaming_increment("Hello").normal_text, "Hello"
)
def test_plain_reply_stream_untouched(self):
detector = self._detector()
out = detector.parse_streaming_increment("Hello")
self.assertEqual(out.normal_text, "Hello")
def test_minimax_m3_model_type(self):
from sglang.srt.parser.reasoning_parser import MiniMaxM3Detector
parser = ReasoningParser("minimax-m3")
self.assertIsInstance(parser.detector, MiniMaxM3Detector)
def test_force_nonempty_content_via_chat_template_kwargs(self):
"""force_nonempty_content must reach the M3 detector without a TypeError."""
from sglang.srt.entrypoints.openai.protocol import (
ChatCompletionMessageUserParam,
ChatCompletionRequest,
)
request = ChatCompletionRequest(
model="test",
messages=[ChatCompletionMessageUserParam(role="user", content="Hi")],
chat_template_kwargs={"force_nonempty_content": True},
)
parser = ReasoningParser("minimax-m3", request=request)
self.assertTrue(parser.detector._force_nonempty_content)
def test_force_nonempty_content_swaps_when_no_content(self):
from sglang.srt.parser.reasoning_parser import MiniMaxM3Detector
detector = MiniMaxM3Detector(force_reasoning=True, force_nonempty_content=True)
result = detector.detect_and_parse("only reasoning, no closer")
self.assertEqual(result.normal_text, "only reasoning, no closer")
self.assertEqual(result.reasoning_text or "", "")
class TestReasoningParserAdvanced(CustomTestCase):
"""Additional tests for ReasoningParser init edge cases."""
def test_gpt_oss_model_type(self):
"""Test that gpt-oss model type creates GptOssDetector."""
from sglang.srt.parser.reasoning_parser import GptOssDetector
parser = ReasoningParser("gpt-oss")
self.assertIsInstance(parser.detector, GptOssDetector)
def test_minimax_append_think_model_type(self):
"""Test that minimax-append-think creates MiniMaxAppendThinkDetector."""
from sglang.srt.parser.reasoning_parser import MiniMaxAppendThinkDetector
parser = ReasoningParser("minimax-append-think")
self.assertIsInstance(parser.detector, MiniMaxAppendThinkDetector)
def test_qwen3_thinking_forces_reasoning(self):
"""Test that qwen3-thinking model type forces reasoning mode."""
parser = ReasoningParser("qwen3-thinking")
self.assertTrue(parser.detector._in_reasoning)
def test_minimax_forces_reasoning(self):
"""Test that minimax model type forces reasoning mode.
minimax maps to Qwen3Detector but ReasoningParser overrides
force_reasoning=True, unlike the default Qwen3Detector behavior.
"""
parser = ReasoningParser("minimax")
self.assertIsInstance(parser.detector, Qwen3Detector)
self.assertTrue(parser.detector._in_reasoning)
def test_detector_map_aliases(self):
"""Test that all DetectorMap alias keys create the correct detector type."""
# These are aliases that map to existing detector classes
alias_tests = {
"deepseek-v3": Qwen3Detector,
"step3": DeepSeekR1Detector,
"step3p5": DeepSeekR1Detector,
"interns1": Qwen3Detector,
}
for model_type, expected_class in alias_tests.items():
parser = ReasoningParser(model_type)
self.assertIsInstance(
parser.detector,
expected_class,
f"{model_type} should create {expected_class.__name__}",
)
def test_continue_final_message_with_request(self):
"""Test continue_final_message passes previous content to detector."""
from sglang.srt.entrypoints.openai.protocol import (
ChatCompletionMessageGenericParam,
ChatCompletionMessageUserParam,
ChatCompletionRequest,
)
request = ChatCompletionRequest(
model="test",
messages=[
ChatCompletionMessageUserParam(role="user", content="Hi"),
ChatCompletionMessageGenericParam(
role="assistant", content="Let me think..."
),
],
continue_final_message=True,
)
parser = ReasoningParser("qwen3", request=request)
self.assertTrue(parser.detector.continue_final_message)
def test_force_nonempty_content_via_chat_template_kwargs(self):
"""Test that force_nonempty_content is passed via chat_template_kwargs."""
from sglang.srt.entrypoints.openai.protocol import (
ChatCompletionMessageUserParam,
ChatCompletionRequest,
)
request = ChatCompletionRequest(
model="test",
messages=[
ChatCompletionMessageUserParam(role="user", content="Hi"),
],
chat_template_kwargs={"force_nonempty_content": True},
)
parser = ReasoningParser("nemotron_3", request=request)
self.assertTrue(parser.detector._force_nonempty_content)
class TestContinueFinalMessage(CustomTestCase):
"""Test continue_final_message mode for BaseReasoningFormatDetector."""
def test_continue_with_think_start_in_previous(self):
"""Test that previous_content with sets _in_reasoning=True."""
detector = BaseReasoningFormatDetector(
"",
"",
force_reasoning=False,
continue_final_message=True,
previous_content="some reasoning",
)
self.assertTrue(detector._in_reasoning)
self.assertEqual(detector.previous_count, len("some reasoning"))
def test_continue_with_think_end_in_previous(self):
"""Test that previous_content with sets _in_reasoning=False."""
detector = BaseReasoningFormatDetector(
"",
"",
force_reasoning=True,
continue_final_message=True,
previous_content="donenormal",
)
# think_end_token in previous → _in_reasoning = False
self.assertFalse(detector._in_reasoning)
def test_continue_detect_parse_with_end_in_previous(self):
"""Test detect_and_parse when think_end_token is in previous_content only.
This covers the branch where think_end is NOT in current text
but IS in previous_content, so output is treated as normal_text."""
detector = BaseReasoningFormatDetector(
"",
"",
force_reasoning=True,
continue_final_message=True,
previous_content="reasoning",
)
# _in_reasoning is False (think_end in previous)
# But force_reasoning was True → detect_and_parse still enters the
# reasoning path because think_start is in previous_content.
# However, since _in_reasoning=False and no think_start in new text,
# it returns normal_text directly.
result = detector.detect_and_parse("new content here")
self.assertEqual(result.normal_text, "new content here")
def test_continue_end_in_previous_new_text_has_start_but_no_end(self):
"""Test: think_end in previous, new text has think_start but no think_end.
This produces: in_reasoning=True (from think_start in text),
think_end NOT in processed_text, think_end IN previous_content,
so it falls through to the else branch that returns normal_text."""
detector = BaseReasoningFormatDetector(
"",
"",
force_reasoning=False,
continue_final_message=True,
previous_content="earlier oldold answer",
)
# _in_reasoning = False (think_end in previous overrides)
self.assertFalse(detector._in_reasoning)
# New text has (triggers in_reasoning) but no
# think_end IS in previous_content → skips the truncated-reasoning branch
# think_end NOT in processed_text → falls to else that returns normal_text
result = detector.detect_and_parse("continuing reasoning")
self.assertEqual(result.normal_text, "continuing reasoning")
self.assertEqual(result.reasoning_text, "")
def test_continue_detect_parse_think_start_in_prev_but_end_also_in_prev(self):
"""Test detect_and_parse where both think tokens are in previous,
and new text contains to re-enter reasoning."""
detector = BaseReasoningFormatDetector(
"",
"",
force_reasoning=False,
continue_final_message=True,
previous_content="old reasoningold answer",
)
# _in_reasoning = False (end token in previous overrides start)
self.assertFalse(detector._in_reasoning)
# New text starts a fresh reasoning block
result = detector.detect_and_parse("new reasoningnew answer")
self.assertEqual(result.reasoning_text, "new reasoning")
self.assertEqual(result.normal_text, "new answer")
class TestGptOssDetectorToolCall(CustomTestCase):
"""Test GptOssDetector tool_call raw_text handling."""
def test_detect_and_parse_tool_call_raw_text(self):
"""Test that tool_call events use raw_text when available."""
from sglang.srt.parser.reasoning_parser import GptOssDetector
detector = GptOssDetector()
# Sequence with CALL...RETURN that produces tool_call events with raw_text
text = (
"<|start|><|channel|>analysis<|message|>think<|end|>"
"<|call|>function_data<|return|>"
"<|channel|>final<|message|>result<|end|>"
)
result = detector.detect_and_parse(text)
self.assertIn("think", result.reasoning_text)
# Tool call raw_text and/or final result should be in normal_text
self.assertIn("result", result.normal_text)
def test_streaming_tool_call_raw_text(self):
"""Test streaming parse with tool_call events preserving raw_text."""
from sglang.srt.parser.reasoning_parser import GptOssDetector
detector = GptOssDetector()
chunks = [
"<|start|><|channel|>analysis<|message|>reason<|end|>",
"<|call|>tool_payload<|return|>",
"<|channel|>final<|message|>done<|end|>",
]
all_reasoning = ""
all_normal = ""
for chunk in chunks:
result = detector.parse_streaming_increment(chunk)
all_reasoning += result.reasoning_text
all_normal += result.normal_text
self.assertIn("reason", all_reasoning)
self.assertIn("done", all_normal)
class TestPoolsideV1Registered(CustomTestCase):
"""poolside_v1 (Laguna-XS.2) reuses the Qwen3 `...` envelope.
Request dispatch differs (Mimo-style explicit `enable_thinking=True`,
asserted in test_serving_chat.py), driven by
`reasoning_default = "explicit_enable_thinking"` on the detector."""
def test_registered_to_qwen3_subclass(self):
cls = ReasoningParser.DetectorMap["poolside_v1"]
self.assertTrue(issubclass(cls, Qwen3Detector))
def test_explicit_enable_thinking_default(self):
rp = ReasoningParser("poolside_v1", stream_reasoning=True)
self.assertEqual(rp.detector.reasoning_default, "explicit_enable_thinking")
self.assertTrue(rp.detector.thinks_internally)
class TestCohereCommand4DetectorFinish(CustomTestCase):
"""finish() flush for Cohere's custom streaming state machine.
This detector pins _in_reasoning True for its whole run and tracks phase via
_reasoning_done, so it overrides finish() rather than inheriting the base
one, which keys on _in_reasoning."""
def test_finish_flushes_truncated_reasoning(self):
"""Stream cut mid-thinking (stream_reasoning=False, no <|END_THINKING|>)
must flush the buffered trace as reasoning instead of dropping it."""
detector = CohereCommand4Detector(stream_reasoning=False)
self.assertEqual(
detector.parse_streaming_increment("partial thinking").reasoning_text,
"",
)
end = detector.finish()
self.assertEqual(end.reasoning_text, "partial thinking")
self.assertEqual(end.normal_text, "")
def test_finish_flushes_answer_tail_as_normal_text(self):
"""Regression guard for the base-class fix: once reasoning has closed, a
truncated answer tail (stream ended before <|END_TEXT|>) must be flushed
as normal_text. The base finish() keyed on _in_reasoning would misfile it
as reasoning because this detector never clears _in_reasoning."""
detector = CohereCommand4Detector(stream_reasoning=False)
detector.parse_streaming_increment("thinking<|END_THINKING|>")
detector.parse_streaming_increment("<|START_TEXT|>the answer")
end = detector.finish()
self.assertEqual(end.normal_text, "the answer")
self.assertEqual(end.reasoning_text, "")
if __name__ == "__main__":
unittest.main()