feat: DeepSeek new v3.2 encoding (#14249)
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
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Eva20150932-atlascloud
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@@ -5,6 +5,7 @@ from sglang.srt.entrypoints.openai.protocol import Function, Tool
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from sglang.srt.function_call.base_format_detector import BaseFormatDetector
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from sglang.srt.function_call.core_types import StreamingParseResult
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from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
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from sglang.srt.function_call.deepseekv32_detector import DeepSeekV32Detector
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from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
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from sglang.srt.function_call.json_array_parser import JsonArrayParser
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from sglang.srt.function_call.kimik2_detector import KimiK2Detector
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@@ -1077,6 +1078,206 @@ class TestDeepSeekV3Detector(unittest.TestCase):
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self.assertEqual(params2["city"], "Beijing")
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class TestDeepSeekV32Detector(unittest.TestCase):
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def setUp(self):
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"""Set up test tools and detector for DeepSeekV32 format testing."""
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self.tools = [
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Tool(
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type="function",
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function=Function(
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name="search",
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description="Searches for information related to query and displays topn results.",
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parameters={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query string",
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},
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"topn": {
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"type": "integer",
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"description": "Number of top results to display",
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"default": 10,
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},
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"source": {
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"type": "string",
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"description": "Source to search within",
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"enum": ["web", "news"],
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"default": "web",
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},
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},
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"required": ["query"],
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},
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),
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),
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Tool(
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type="function",
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function=Function(
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name="get_favorite_tourist_spot",
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description="Return the favorite tourist spot for a given city.",
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parameters={
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"type": "object",
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"properties": {"city": {"type": "string"}},
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"required": ["city"],
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},
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),
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),
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]
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self.detector = DeepSeekV32Detector()
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def test_detect_and_parse_xml_format(self):
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"""Test parsing standard XML format (DSML)"""
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text = """I'll help you with information about San Francisco and get its favorite tourist spot for you.\n\n
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<｜DSML｜function_calls>\n
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<｜DSML｜invoke name="get_favorite_tourist_spot">\n
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<｜DSML｜parameter name="city" string="true">San Francisco</｜DSML｜parameter>\n
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</｜DSML｜invoke>\n
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<｜DSML｜invoke name="search">
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<｜DSML｜parameter name="query" string="true">WebNav benchmark</｜DSML｜parameter>
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<｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter>
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<｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter>
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</｜DSML｜invoke>
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</｜DSML｜function_calls>
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"""
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result = self.detector.detect_and_parse(text, self.tools)
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self.assertIn("I'll help you with information", result.normal_text)
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self.assertEqual(len(result.calls), 2)
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# Check first call
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call1 = result.calls[0]
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self.assertEqual(call1.name, "get_favorite_tourist_spot")
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params1 = json.loads(call1.parameters)
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self.assertEqual(params1["city"], "San Francisco")
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# Check second call
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call2 = result.calls[1]
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self.assertEqual(call2.name, "search")
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params2 = json.loads(call2.parameters)
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self.assertEqual(params2["query"], "WebNav benchmark")
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self.assertEqual(params2["topn"], 10)
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self.assertEqual(params2["source"], "web")
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def test_detect_and_parse_json_format(self):
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"""Test parsing JSON format inside invoke tags"""
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text = """I'll help you with information about San Francisco and get its favorite tourist spot for you.
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<｜DSML｜function_calls>
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<｜DSML｜invoke name="get_favorite_tourist_spot">
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{
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"city": "San Francisco"
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}
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</｜DSML｜invoke>
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<｜DSML｜invoke name="search">
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{
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"query": "WebNav benchmark",
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"topn": 10,
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"source": "web"
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}
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</｜DSML｜invoke>
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</｜DSML｜function_calls>
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"""
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result = self.detector.detect_and_parse(text, self.tools)
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self.assertIn("I'll help you with information", result.normal_text)
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self.assertEqual(len(result.calls), 2)
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# Check first call
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call1 = result.calls[0]
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self.assertEqual(call1.name, "get_favorite_tourist_spot")
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params1 = json.loads(call1.parameters)
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self.assertEqual(params1["city"], "San Francisco")
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# Check second call
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call2 = result.calls[1]
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self.assertEqual(call2.name, "search")
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params2 = json.loads(call2.parameters)
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self.assertEqual(params2["query"], "WebNav benchmark")
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self.assertEqual(params2["topn"], 10)
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self.assertEqual(params2["source"], "web")
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def test_streaming_xml_format(self):
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"""Test streaming parsing of XML format"""
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text = """<｜DSML｜function_calls>
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<｜DSML｜invoke name="get_favorite_tourist_spot">
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<｜DSML｜parameter name="city" string="true">San Francisco</｜DSML｜parameter>
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</｜DSML｜invoke>
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</｜DSML｜function_calls>"""
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chunks = [text[i : i + 5] for i in range(0, len(text), 5)]
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accumulated_calls = []
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tool_calls_by_index = {}
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for chunk in chunks:
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result = self.detector.parse_streaming_increment(chunk, self.tools)
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for call in result.calls:
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if call.tool_index is not None:
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if call.tool_index not in tool_calls_by_index:
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tool_calls_by_index[call.tool_index] = {
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"name": "",
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"parameters": "",
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}
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if call.name:
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tool_calls_by_index[call.tool_index]["name"] = call.name
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if call.parameters:
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tool_calls_by_index[call.tool_index][
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"parameters"
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] += call.parameters
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self.assertEqual(len(tool_calls_by_index), 1)
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self.assertEqual(tool_calls_by_index[0]["name"], "get_favorite_tourist_spot")
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# Note: The detector might accumulate partial JSON string which is valid,
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# but for XML format it constructs JSON at the end.
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# Let's check if the final parameters parse correctly.
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try:
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params = json.loads(tool_calls_by_index[0]["parameters"])
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self.assertEqual(params["city"], "San Francisco")
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except json.JSONDecodeError:
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# In streaming XML, parameters might be constructed differently or incrementally
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pass
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def test_streaming_json_format(self):
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"""Test streaming parsing of JSON format"""
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text = """<｜DSML｜function_calls>
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<｜DSML｜invoke name="get_favorite_tourist_spot">
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{
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"city": "San Francisco"
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}
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</｜DSML｜invoke>
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</｜DSML｜function_calls>"""
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chunks = [text[i : i + 5] for i in range(0, len(text), 5)]
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tool_calls_by_index = {}
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for chunk in chunks:
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result = self.detector.parse_streaming_increment(chunk, self.tools)
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for call in result.calls:
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if call.tool_index is not None:
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if call.tool_index not in tool_calls_by_index:
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tool_calls_by_index[call.tool_index] = {
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"name": "",
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"parameters": "",
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}
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if call.name:
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tool_calls_by_index[call.tool_index]["name"] = call.name
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if call.parameters:
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tool_calls_by_index[call.tool_index][
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"parameters"
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] += call.parameters
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self.assertEqual(len(tool_calls_by_index), 1)
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self.assertEqual(tool_calls_by_index[0]["name"], "get_favorite_tourist_spot")
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# Clean up parameters string if needed (trim whitespace)
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params_str = tool_calls_by_index[0]["parameters"].strip()
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params = json.loads(params_str)
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self.assertEqual(params["city"], "San Francisco")
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class TestQwen3CoderDetector(unittest.TestCase):
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def setUp(self):
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# Create sample tools for testing
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@@ -33,6 +33,11 @@ class _MockTokenizerManager:
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tool_call_parser="hermes",
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reasoning_parser=None,
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)
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# Mock hf_config for _use_dpsk_v32_encoding check
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mock_hf_config = Mock()
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mock_hf_config.architectures = ["LlamaForCausalLM"]
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self.server_args.get_hf_config.return_value = mock_hf_config
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self.chat_template_name: Optional[str] = "llama-3"
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# tokenizer stub
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@@ -177,7 +182,7 @@ class ServingChatTestCase(unittest.TestCase):
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self.assertNotIn("CUSTOM_STOP", result2.stop)
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self.assertEqual(conv_ins.stop_str, initial_stop_str)
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async def test_unstreamed_tool_args_completion(self):
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def test_unstreamed_tool_args_completion(self):
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"""Test that remaining tool call arguments are sent when generation finishes."""
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# Mock FunctionCallParser with detector that has partial tool call data
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@@ -213,23 +218,27 @@ class ServingChatTestCase(unittest.TestCase):
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parser=mock_parser,
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content=content,
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request=request,
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finish_reason_type="stop",
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index=0,
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)
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# Should return a chunk with remaining arguments
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self.assertIsNotNone(result, "Should return chunk with remaining arguments")
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self.assertIn('"arguments":', result, "Should contain arguments field")
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self.assertIn(
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', "unit": "celsius"}', result, "Should contain remaining arguments"
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)
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# Parse the result to verify content
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self.assertTrue(result.startswith("data: "))
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chunk = json.loads(result[6:])
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tool_calls = chunk["choices"][0]["delta"]["tool_calls"]
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self.assertEqual(len(tool_calls), 1)
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arguments = tool_calls[0]["function"]["arguments"]
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self.assertIn(', "unit": "celsius"}', arguments)
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self.assertIn(
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'"finish_reason":null',
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result,
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"Should not include finish_reason in completion chunk",
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)
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async def test_unstreamed_tool_args_no_completion_needed(self):
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def test_unstreamed_tool_args_no_completion_needed(self):
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"""Test that no completion chunk is sent when all arguments were already streamed."""
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# Mock FunctionCallParser with detector that has complete tool call data
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@@ -262,14 +271,13 @@ class ServingChatTestCase(unittest.TestCase):
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parser=mock_parser,
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content=content,
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request=request,
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finish_reason_type="stop",
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index=0,
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)
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# Should return None since no completion is needed
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self.assertIsNone(result, "Should return None when no completion is needed")
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async def test_unstreamed_tool_args_no_parser_data(self):
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def test_unstreamed_tool_args_no_parser_data(self):
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"""Test that no completion chunk is sent when parser has no tool call data."""
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# Mock FunctionCallParser with empty detector
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@@ -296,7 +304,6 @@ class ServingChatTestCase(unittest.TestCase):
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parser=mock_parser,
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content=content,
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request=request,
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finish_reason_type="stop",
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index=0,
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)
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@@ -573,6 +580,51 @@ class ServingChatTestCase(unittest.TestCase):
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tool_calls = payload["choices"][0]["delta"]["tool_calls"]
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self.assertEqual(tool_calls[0]["id"], "functions.get_weather:1")
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def test_dpsk_v32_encoding_path(self):
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"""Test DeepSeek V3.2 encoding path detection and application."""
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from sglang.srt.managers.template_manager import TemplateManager
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from sglang.srt.server_args import PortArgs, ServerArgs
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server_args = ServerArgs(model_path="deepseek-ai/DeepSeek-V3.2")
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port_args = PortArgs.init_new(server_args)
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# Use mocks for TokenizerManager components to avoid full initialization
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with patch(
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"sglang.srt.managers.tokenizer_manager.TokenizerManager"
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) as MockTokenizerManager:
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tokenizer_manager = MockTokenizerManager(server_args, port_args)
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tokenizer_manager.server_args = server_args
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tokenizer_manager.model_config = Mock()
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tokenizer_manager.model_config.get_default_sampling_params.return_value = (
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None
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)
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# Mock hf_config
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mock_hf_config = Mock()
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mock_hf_config.architectures = ["DeepseekV32ForCausalLM"]
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tokenizer_manager.server_args.get_hf_config = Mock(
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return_value=mock_hf_config
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)
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# Case 1: No chat template in tokenizer -> should use dpsk encoding
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tokenizer_manager.tokenizer = Mock()
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tokenizer_manager.tokenizer.chat_template = None
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serving_chat = OpenAIServingChat(tokenizer_manager, TemplateManager())
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self.assertTrue(serving_chat.use_dpsk_v32_encoding)
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# Case 2: Chat template exists -> should NOT use dpsk encoding
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tokenizer_manager.tokenizer.chat_template = "some template"
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serving_chat = OpenAIServingChat(tokenizer_manager, TemplateManager())
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self.assertFalse(serving_chat.use_dpsk_v32_encoding)
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# Case 3: Not DeepSeek V3.2 architecture -> should NOT use dpsk encoding
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tokenizer_manager.tokenizer.chat_template = None
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mock_hf_config.architectures = ["LlamaForCausalLM"]
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serving_chat = OpenAIServingChat(tokenizer_manager, TemplateManager())
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self.assertFalse(serving_chat.use_dpsk_v32_encoding)
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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