sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct
This commit is contained in:
maxiao1
0
test/srt/openai_server/function_call/__init__.py
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0
test/srt/openai_server/function_call/__init__.py
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@@ -0,0 +1,953 @@
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import json
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import time
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import unittest
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import openai
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from sglang.srt.hf_transformers_utils import get_tokenizer
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from sglang.srt.utils import kill_process_tree
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from sglang.test.test_utils import (
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DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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popen_launch_server,
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)
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class TestOpenAIServerFunctionCalling(CustomTestCase):
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# NOTE: this system_message is for Llama3.2 system prompt. Without this,
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# sometimes Llama3.2 gives a different tool call format such as:
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# '<|python_tag|>{"type": "function", "function": "add", "parameters": {"a": "3", "b": "5"}}'
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SYSTEM_MESSAGE = (
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"You are a helpful assistant with tool calling capabilities. "
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"Only reply with a tool call if the function exists in the library provided by the user. "
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"If it doesn't exist, just reply directly in natural language. "
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"When you receive a tool call response, use the output to format an answer to the original user question. "
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"You have access to the following functions. "
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"To call a function, please respond with JSON for a function call. "
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'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. '
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"Do not use variables.\n\n"
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)
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@classmethod
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def setUpClass(cls):
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# Replace with the model name needed for testing; if not required, reuse DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.api_key = "sk-123456"
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# Start the local OpenAI Server. If necessary, you can add other parameters such as --enable-tools.
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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api_key=cls.api_key,
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other_args=[
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# If your server needs extra parameters to test function calling, please add them here.
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"--tool-call-parser",
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"llama3",
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],
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)
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cls.base_url += "/v1"
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cls.tokenizer = get_tokenizer(cls.model)
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@classmethod
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def tearDownClass(cls):
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kill_process_tree(cls.process.pid)
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def test_function_calling_format(self):
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"""
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Test: Whether the function call format returned by the AI is correct.
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When returning a tool call, message.content should be None, and tool_calls should be a list.
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"""
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "add",
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"description": "Compute the sum of two numbers",
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"parameters": {
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"type": "object",
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"properties": {
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"a": {
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"type": "int",
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"description": "A number",
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},
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"b": {
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"type": "int",
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"description": "A number",
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},
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},
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"required": ["a", "b"],
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},
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},
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}
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]
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messages = [
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{"role": "system", "content": self.SYSTEM_MESSAGE},
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{"role": "user", "content": "Compute (3+5)"},
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]
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response = client.chat.completions.create(
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model=self.model,
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max_tokens=2048,
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messages=messages,
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temperature=0.8,
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top_p=0.8,
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stream=False,
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tools=tools,
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)
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tool_calls = response.choices[0].message.tool_calls
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assert (
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isinstance(tool_calls, list) and len(tool_calls) > 0
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), "tool_calls should be a non-empty list"
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function_name = tool_calls[0].function.name
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assert function_name == "add", "Function name should be 'add'"
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# This unit test is too difficult for default model. Mark it as optional unit tests so it won't trigger unless specified.
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def _test_function_calling_multiturn(self):
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"""
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Test: Whether the function call format returned by the AI is correct.
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When returning a tool call, message.content should be None, and tool_calls should be a list.
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"""
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "add",
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"description": "Compute the sum of two numbers",
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"parameters": {
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"type": "object",
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"properties": {
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"a": {
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"type": "int",
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"description": "A number",
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},
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"b": {
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"type": "int",
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"description": "A number",
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},
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},
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"required": ["a", "b"],
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},
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},
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}
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]
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messages = [{"role": "user", "content": "Compute (3+5)"}]
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response = client.chat.completions.create(
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model=self.model,
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max_tokens=2048,
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messages=messages,
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temperature=0.8,
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top_p=0.8,
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stream=False,
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tools=tools,
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)
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tool_call = response.choices[0].message.tool_calls[0]
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function_name = tool_call.function.name
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assert function_name == "add", "Function name should be 'add'"
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function_arguments = tool_call.function.arguments
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function_arguments = json.loads(tool_call.function.arguments)
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assert function_arguments in [
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{"a": 3, "b": 5},
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{"a": "3", "b": "5"},
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], f"Unexpected function arguments: {function_arguments}"
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messages.append(response.choices[0].message)
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messages.append(
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{
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": "8",
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"name": function_name,
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}
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)
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final_response = client.chat.completions.create(
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model=self.model,
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max_tokens=2048,
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messages=messages,
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temperature=0.8,
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top_p=0.8,
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stream=False,
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tools=tools,
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)
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assert (
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"8" in final_response.choices[0].message.content
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), "tool_call response should have the sum 8 in the content"
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def test_function_calling_streaming_simple(self):
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"""
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Test: Whether the function name can be correctly recognized in streaming mode.
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- Expect a function call to be found, and the function name to be correct.
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- Verify that streaming mode returns at least multiple chunks.
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"""
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "The city to find the weather for",
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},
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"unit": {
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"type": "string",
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"description": "Weather unit (celsius or fahrenheit)",
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"enum": ["celsius", "fahrenheit"],
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},
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},
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"required": ["city", "unit"],
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},
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},
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}
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]
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messages = [
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{"role": "system", "content": self.SYSTEM_MESSAGE},
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{
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"role": "user",
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"content": "What is the temperature in Paris in celsius??",
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},
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]
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response_stream = client.chat.completions.create(
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model=self.model,
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max_tokens=2048,
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messages=messages,
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temperature=0.8,
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top_p=0.8,
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stream=True,
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tools=tools,
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)
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chunks = list(response_stream)
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self.assertTrue(len(chunks) > 0, "Streaming should return at least one chunk")
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found_function_name = False
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for chunk in chunks:
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choice = chunk.choices[0]
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# Check whether the current chunk contains tool_calls
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if choice.delta.tool_calls:
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tool_call = choice.delta.tool_calls[0]
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if tool_call.function.name:
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self.assertEqual(
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tool_call.function.name,
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"get_current_weather",
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"Function name should be 'get_current_weather'",
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)
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found_function_name = True
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break
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self.assertTrue(
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found_function_name,
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"Target function name 'get_current_weather' was not found in the streaming chunks",
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)
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finish_reason = chunks[-1].choices[0].finish_reason
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self.assertEqual(
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finish_reason,
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"tool_calls",
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"Final response of function calling should have finish_reason 'tool_calls'",
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)
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def test_function_calling_streaming_args_parsing(self):
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"""
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Test: Whether the function call arguments returned in streaming mode can be correctly concatenated into valid JSON.
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- The user request requires multiple parameters.
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- AI may return the arguments in chunks that need to be concatenated.
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"""
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "add",
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"description": "Compute the sum of two integers",
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"parameters": {
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"type": "object",
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"properties": {
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"a": {
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"type": "integer",
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"description": "First integer",
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},
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"b": {
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"type": "integer",
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"description": "Second integer",
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},
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},
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"required": ["a", "b"],
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},
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"strict": True, # Llama-3.2-1B is flaky in tool call. It won't always respond with parameters unless we set strict.
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},
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}
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]
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messages = [
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{"role": "system", "content": self.SYSTEM_MESSAGE},
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{"role": "user", "content": "Please sum 5 and 7, just call the function."},
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]
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response_stream = client.chat.completions.create(
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model=self.model,
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max_tokens=2048,
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messages=messages,
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temperature=0.9,
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top_p=0.9,
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stream=True,
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tools=tools,
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)
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argument_fragments = []
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chunks = list(response_stream)
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function_name = None
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for chunk in chunks:
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choice = chunk.choices[0]
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if choice.delta.tool_calls:
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tool_call = choice.delta.tool_calls[0]
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# Record the function name on first occurrence
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function_name = tool_call.function.name or function_name
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# In case of multiple chunks, JSON fragments may need to be concatenated
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if tool_call.function.arguments is not None:
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argument_fragments.append(tool_call.function.arguments)
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self.assertEqual(function_name, "add", "Function name should be 'add'")
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joined_args = "".join(argument_fragments)
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self.assertTrue(
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len(joined_args) > 0,
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"No parameter fragments were returned in the function call",
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)
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finish_reason = chunks[-1].choices[0].finish_reason
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self.assertEqual(
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finish_reason,
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"tool_calls",
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"Final response of function calling should have finish_reason 'tool_calls'",
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)
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# Check whether the concatenated JSON is valid
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try:
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args_obj = json.loads(joined_args)
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except json.JSONDecodeError:
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self.fail(
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"The concatenated tool call arguments are not valid JSON, parsing failed"
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)
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self.assertIn("a", args_obj, "Missing parameter 'a'")
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self.assertIn("b", args_obj, "Missing parameter 'b'")
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self.assertEqual(str(args_obj["a"]), "5", "Parameter a should be 5")
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self.assertEqual(str(args_obj["b"]), "7", "Parameter b should be 7")
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def test_function_call_strict(self):
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"""
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Test: Whether the strict mode of function calling works as expected.
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- When strict mode is enabled, the AI should not return a function call if the function name is not recognized.
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"""
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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tools = [
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{
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"type": "function",
|
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"function": {
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"name": "sub",
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"description": "Compute the difference of two integers",
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"parameters": {
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"type": "object",
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"properties": {
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"int_a": {
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"type": "integer",
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"description": "First integer",
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},
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"int_b": {
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"type": "integer",
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"description": "Second integer",
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||||
},
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},
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"required": ["int_a", "int_b"],
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||||
},
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"strict": True,
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},
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||||
}
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]
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messages = [
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{"role": "user", "content": "Please compute 5 - 7, using your tool."}
|
||||
]
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response = client.chat.completions.create(
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model=self.model,
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max_tokens=2048,
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||||
messages=messages,
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temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=False,
|
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tools=tools,
|
||||
)
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tool_calls = response.choices[0].message.tool_calls
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function_name = tool_calls[0].function.name
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arguments = tool_calls[0].function.arguments
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args_obj = json.loads(arguments)
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self.assertEqual(function_name, "sub", "Function name should be 'sub'")
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self.assertEqual(str(args_obj["int_a"]), "5", "Parameter int_a should be 5")
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self.assertEqual(str(args_obj["int_b"]), "7", "Parameter int_b should be 7")
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||||
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def test_function_call_required(self):
|
||||
"""
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||||
Test: Whether tool_choice: "required" works as expected
|
||||
- When tool_choice == "required", the model should return one or more tool_calls.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "sub",
|
||||
"description": "Compute the difference of two integers",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"int_a": {
|
||||
"type": "integer",
|
||||
"description": "First integer",
|
||||
},
|
||||
"int_b": {
|
||||
"type": "integer",
|
||||
"description": "Second integer",
|
||||
},
|
||||
},
|
||||
"required": ["int_a", "int_b"],
|
||||
},
|
||||
"strict": True,
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "use this to get latest weather information for a city given its name",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "name of the city to get weather for",
|
||||
}
|
||||
},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
messages = [{"role": "user", "content": "What is the capital of France?"}]
|
||||
response = client.chat.completions.create(
|
||||
model=self.model,
|
||||
max_tokens=2048,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=False,
|
||||
tools=tools,
|
||||
tool_choice="required",
|
||||
)
|
||||
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
self.assertIsNotNone(tool_calls, "No tool_calls in the response")
|
||||
function_name = tool_calls[0].function.name
|
||||
arguments = tool_calls[0].function.arguments
|
||||
args_obj = json.loads(arguments)
|
||||
|
||||
self.assertEqual(
|
||||
function_name,
|
||||
"get_weather",
|
||||
f"Function name should be 'get_weather', got: {function_name}",
|
||||
)
|
||||
self.assertIn(
|
||||
"city", args_obj, f"Function arguments should have 'city', got: {args_obj}"
|
||||
)
|
||||
|
||||
# Make the test more robust by checking type and accepting valid responses
|
||||
city_value = args_obj["city"]
|
||||
self.assertIsInstance(
|
||||
city_value,
|
||||
str,
|
||||
f"Parameter city should be a string, got: {type(city_value)}",
|
||||
)
|
||||
self.assertTrue(
|
||||
"Paris" in city_value or "France" in city_value,
|
||||
f"Parameter city should contain either 'Paris' or 'France', got: {city_value}",
|
||||
)
|
||||
|
||||
def test_function_call_specific(self):
|
||||
"""
|
||||
Test: Whether tool_choice: ToolChoice works as expected
|
||||
- When tool_choice is a specific ToolChoice, the model should return one or more tool_calls.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "sub",
|
||||
"description": "Compute the difference of two integers",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"int_a": {
|
||||
"type": "integer",
|
||||
"description": "First integer",
|
||||
},
|
||||
"int_b": {
|
||||
"type": "integer",
|
||||
"description": "Second integer",
|
||||
},
|
||||
},
|
||||
"required": ["int_a", "int_b"],
|
||||
},
|
||||
"strict": True,
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "use this to get latest weather information for a city given its name",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "name of the city to get weather for",
|
||||
}
|
||||
},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
messages = [{"role": "user", "content": "What is the capital of France?"}]
|
||||
response = client.chat.completions.create(
|
||||
model=self.model,
|
||||
max_tokens=2048,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=False,
|
||||
tools=tools,
|
||||
tool_choice={"type": "function", "function": {"name": "get_weather"}},
|
||||
)
|
||||
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
self.assertIsNotNone(tool_calls, "No tool_calls in the response")
|
||||
function_name = tool_calls[0].function.name
|
||||
arguments = tool_calls[0].function.arguments
|
||||
args_obj = json.loads(arguments)
|
||||
|
||||
self.assertEqual(
|
||||
function_name, "get_weather", "Function name should be 'get_weather'"
|
||||
)
|
||||
self.assertIn("city", args_obj, "Function arguments should have 'city'")
|
||||
|
||||
def test_streaming_multiple_choices_finish_reason(self):
|
||||
"""
|
||||
Test: Verify that each choice gets its own finish_reason chunk in streaming mode with n > 1.
|
||||
This tests the fix for the bug where only the last index got a finish_reason chunk.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "What is the weather like in Los Angeles?"}
|
||||
]
|
||||
|
||||
# Request with n=2 to get multiple choices
|
||||
response_stream = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
temperature=0.8,
|
||||
stream=True,
|
||||
tools=tools,
|
||||
tool_choice="required", # Force tool calls
|
||||
n=2, # Multiple choices
|
||||
)
|
||||
|
||||
chunks = list(response_stream)
|
||||
|
||||
# Track finish_reason chunks for each index
|
||||
finish_reason_chunks = {}
|
||||
for chunk in chunks:
|
||||
if chunk.choices:
|
||||
for choice in chunk.choices:
|
||||
if choice.finish_reason is not None:
|
||||
index = choice.index
|
||||
if index not in finish_reason_chunks:
|
||||
finish_reason_chunks[index] = []
|
||||
finish_reason_chunks[index].append(choice.finish_reason)
|
||||
|
||||
# Verify we got finish_reason chunks for both indices
|
||||
self.assertEqual(
|
||||
len(finish_reason_chunks),
|
||||
2,
|
||||
f"Expected finish_reason chunks for 2 indices, got {len(finish_reason_chunks)}",
|
||||
)
|
||||
|
||||
# Verify both index 0 and 1 have finish_reason
|
||||
self.assertIn(
|
||||
0, finish_reason_chunks, "Missing finish_reason chunk for index 0"
|
||||
)
|
||||
self.assertIn(
|
||||
1, finish_reason_chunks, "Missing finish_reason chunk for index 1"
|
||||
)
|
||||
|
||||
# Verify the finish_reason is "tool_calls" since we forced tool calls
|
||||
for index, reasons in finish_reason_chunks.items():
|
||||
self.assertEqual(
|
||||
reasons[-1], # Last finish_reason for this index
|
||||
"tool_calls",
|
||||
f"Expected finish_reason 'tool_calls' for index {index}, got {reasons[-1]}",
|
||||
)
|
||||
|
||||
def test_function_calling_streaming_no_tool_call(self):
|
||||
"""
|
||||
Test: Whether the finish_reason is stop in streaming mode when no tool call is given.
|
||||
- Expect no function call to be found.
|
||||
- Verify that finish_reason is stop
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "The city to find the weather for",
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"description": "Weather unit (celsius or fahrenheit)",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
},
|
||||
},
|
||||
"required": ["city", "unit"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
messages = [{"role": "user", "content": "Who are you?"}]
|
||||
|
||||
response_stream = client.chat.completions.create(
|
||||
model=self.model,
|
||||
max_tokens=2048,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=True,
|
||||
tools=tools,
|
||||
tool_choice="none",
|
||||
)
|
||||
|
||||
chunks = list(response_stream)
|
||||
self.assertTrue(len(chunks) > 0, "Streaming should return at least one chunk")
|
||||
|
||||
found_tool_call = False
|
||||
for chunk in chunks:
|
||||
choice = chunk.choices[0]
|
||||
# Check whether the current chunk contains tool_calls
|
||||
found_tool_call = choice.delta.tool_calls is not None
|
||||
|
||||
self.assertFalse(
|
||||
found_tool_call,
|
||||
"Shouldn't have any tool_call in the streaming chunks",
|
||||
)
|
||||
|
||||
finish_reason = chunks[-1].choices[0].finish_reason
|
||||
self.assertEqual(
|
||||
finish_reason,
|
||||
"stop",
|
||||
"Final response of no function calling should have finish_reason 'stop'",
|
||||
)
|
||||
|
||||
def test_streaming_multiple_choices_without_tools(self):
|
||||
"""
|
||||
Test: Verify that each choice gets its own finish_reason chunk without tool calls.
|
||||
This tests the fix for regular content streaming with multiple choices.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
messages = [{"role": "user", "content": "Say hello in one word."}]
|
||||
|
||||
# Request with n=2 to get multiple choices, no tools
|
||||
response_stream = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
stream=True,
|
||||
max_tokens=10, # Keep it short
|
||||
n=2, # Multiple choices
|
||||
)
|
||||
|
||||
chunks = list(response_stream)
|
||||
|
||||
# Track finish_reason chunks for each index
|
||||
finish_reason_chunks = {}
|
||||
for chunk in chunks:
|
||||
if chunk.choices:
|
||||
for choice in chunk.choices:
|
||||
if choice.finish_reason is not None:
|
||||
index = choice.index
|
||||
if index not in finish_reason_chunks:
|
||||
finish_reason_chunks[index] = []
|
||||
finish_reason_chunks[index].append(choice.finish_reason)
|
||||
|
||||
# Verify we got finish_reason chunks for both indices
|
||||
self.assertEqual(
|
||||
len(finish_reason_chunks),
|
||||
2,
|
||||
f"Expected finish_reason chunks for 2 indices, got {len(finish_reason_chunks)}",
|
||||
)
|
||||
|
||||
# Verify both index 0 and 1 have finish_reason
|
||||
self.assertIn(
|
||||
0, finish_reason_chunks, "Missing finish_reason chunk for index 0"
|
||||
)
|
||||
self.assertIn(
|
||||
1, finish_reason_chunks, "Missing finish_reason chunk for index 1"
|
||||
)
|
||||
|
||||
# Verify the finish_reason is "stop" (regular completion)
|
||||
for index, reasons in finish_reason_chunks.items():
|
||||
self.assertIn(
|
||||
reasons[-1],
|
||||
["stop", "length"], # Could be either depending on how model responds
|
||||
f"Expected finish_reason 'stop' or 'length' for index {index}, got {reasons[-1]}",
|
||||
)
|
||||
|
||||
|
||||
class TestOpenAIPythonicFunctionCalling(CustomTestCase):
|
||||
PYTHONIC_TOOLS = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather for a given location.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The name of the city or location.",
|
||||
}
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_tourist_attractions",
|
||||
"description": "Get a list of top tourist attractions for a given city.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "The name of the city to find attractions for.",
|
||||
}
|
||||
},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
PYTHONIC_MESSAGES = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a travel assistant. "
|
||||
"When asked to call functions, ALWAYS respond ONLY with a python list of function calls, "
|
||||
"using this format: [func_name1(param1=value1, param2=value2), func_name2(param=value)]. "
|
||||
"Do NOT use JSON, do NOT use variables, do NOT use any other format. "
|
||||
"Here is an example:\n"
|
||||
'[get_weather(location="Paris"), get_tourist_attractions(city="Paris")]'
|
||||
),
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"I'm planning a trip to Tokyo next week. What's the weather like and what are some top tourist attractions? "
|
||||
"Propose parallel tool calls at once, using the python list of function calls format as shown above."
|
||||
),
|
||||
},
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
|
||||
cls.base_url = DEFAULT_URL_FOR_TEST
|
||||
cls.api_key = "sk-123456"
|
||||
cls.process = popen_launch_server(
|
||||
cls.model,
|
||||
cls.base_url,
|
||||
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
api_key=cls.api_key,
|
||||
other_args=[
|
||||
"--tool-call-parser",
|
||||
"pythonic",
|
||||
],
|
||||
)
|
||||
cls.base_url += "/v1"
|
||||
cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
kill_process_tree(cls.process.pid)
|
||||
|
||||
def test_pythonic_tool_call_prompt(self):
|
||||
"""
|
||||
Test: Explicit prompt for pythonic tool call format without chat template.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
response = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=self.PYTHONIC_MESSAGES,
|
||||
tools=self.PYTHONIC_TOOLS,
|
||||
temperature=0.1,
|
||||
stream=False,
|
||||
)
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
self.assertIsInstance(tool_calls, list, "No tool_calls found")
|
||||
self.assertGreaterEqual(len(tool_calls), 1)
|
||||
names = [tc.function.name for tc in tool_calls]
|
||||
self.assertTrue(
|
||||
"get_weather" in names or "get_tourist_attractions" in names,
|
||||
f"Function name '{names}' should container either 'get_weather' or 'get_tourist_attractions'",
|
||||
)
|
||||
|
||||
def test_pythonic_tool_call_streaming(self):
|
||||
"""
|
||||
Test: Streaming pythonic tool call format; assert tool_call index is present.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
response_stream = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=self.PYTHONIC_MESSAGES,
|
||||
tools=self.PYTHONIC_TOOLS,
|
||||
temperature=0.1,
|
||||
stream=True,
|
||||
)
|
||||
found_tool_calls = False
|
||||
found_index = False
|
||||
found_names = set()
|
||||
for chunk in response_stream:
|
||||
choice = chunk.choices[0]
|
||||
if getattr(choice.delta, "tool_calls", None):
|
||||
found_tool_calls = True
|
||||
tool_call = choice.delta.tool_calls[0]
|
||||
if hasattr(tool_call, "index") or (
|
||||
isinstance(tool_call, dict) and "index" in tool_call
|
||||
):
|
||||
found_index = True
|
||||
found_names.add(str(tool_call.function.name))
|
||||
|
||||
self.assertTrue(found_tool_calls, "No tool_calls found in streaming response")
|
||||
self.assertTrue(found_index, "No index field found in any streamed tool_call")
|
||||
self.assertTrue(
|
||||
"get_weather" in found_names or "get_tourist_attractions" in found_names,
|
||||
f"Function name '{found_names}' should container either 'get_weather' or 'get_tourist_attractions'",
|
||||
)
|
||||
|
||||
|
||||
# Skip for ci test
|
||||
# class TestGLM45ServerFunctionCalling(TestOpenAIServerFunctionCalling):
|
||||
# @classmethod
|
||||
# def setUpClass(cls):
|
||||
# # Replace with the model name needed for testing; if not required, reuse DEFAULT_SMALL_MODEL_NAME_FOR_TEST
|
||||
# cls.model = "THUDM/GLM-4.5"
|
||||
# cls.base_url = DEFAULT_URL_FOR_TEST
|
||||
# cls.api_key = "sk-123456"
|
||||
|
||||
# # Start the local OpenAI Server. If necessary, you can add other parameters such as --enable-tools.
|
||||
# cls.process = popen_launch_server(
|
||||
# cls.model,
|
||||
# cls.base_url,
|
||||
# timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
# api_key=cls.api_key,
|
||||
# other_args=[
|
||||
# # If your server needs extra parameters to test function calling, please add them here.
|
||||
# "--tool-call-parser",
|
||||
# "glm45",
|
||||
# "--reasoning-parser",
|
||||
# "glm45",
|
||||
# "--tp-size",
|
||||
# "8"
|
||||
# ],
|
||||
# )
|
||||
# cls.base_url += "/v1"
|
||||
# cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
# # This test is too difficult for GLM4-moe. Skip it from the UT
|
||||
# def test_function_call_required(self):
|
||||
# pass
|
||||
|
||||
# def test_function_calling_multiturn(self):
|
||||
# self._test_function_calling_multiturn()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
550
test/srt/openai_server/function_call/test_tool_choice.py
Normal file
550
test/srt/openai_server/function_call/test_tool_choice.py
Normal file
@@ -0,0 +1,550 @@
|
||||
"""
|
||||
Test script for tool_choice functionality in SGLang
|
||||
Tests: required, auto, and specific function choices in both streaming and non-streaming modes
|
||||
|
||||
# To run the tests, use the following command:
|
||||
#
|
||||
# python3 -m unittest openai_server.function_call.test_tool_choice
|
||||
"""
|
||||
|
||||
import json
|
||||
import unittest
|
||||
|
||||
import openai
|
||||
|
||||
from sglang.srt.hf_transformers_utils import get_tokenizer
|
||||
from sglang.srt.utils import kill_process_tree
|
||||
from sglang.test.test_utils import (
|
||||
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
DEFAULT_URL_FOR_TEST,
|
||||
CustomTestCase,
|
||||
popen_launch_server,
|
||||
)
|
||||
|
||||
|
||||
class TestToolChoiceLlama32(CustomTestCase):
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
# Mark flaky tests for this model
|
||||
cls.flaky_tests = {
|
||||
"test_multi_tool_scenario_auto",
|
||||
"test_multi_tool_scenario_required",
|
||||
}
|
||||
|
||||
# Use a model that supports function calling
|
||||
cls.model = "meta-llama/Llama-3.2-1B-Instruct"
|
||||
cls.base_url = DEFAULT_URL_FOR_TEST
|
||||
cls.api_key = "sk-123456"
|
||||
|
||||
# Start the local OpenAI Server with tool calling support
|
||||
cls.process = popen_launch_server(
|
||||
cls.model,
|
||||
cls.base_url,
|
||||
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
api_key=cls.api_key,
|
||||
other_args=[
|
||||
"--tool-call-parser",
|
||||
"llama3", # Default parser for the test model
|
||||
],
|
||||
)
|
||||
cls.base_url += "/v1"
|
||||
cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
kill_process_tree(cls.process.pid)
|
||||
|
||||
def setUp(self):
|
||||
self.client = openai.Client(base_url=self.base_url, api_key=self.api_key)
|
||||
self.model_name = self.client.models.list().data[0].id
|
||||
|
||||
def _is_flaky_test(self):
|
||||
"""Check if the current test is marked as flaky for this class"""
|
||||
return (
|
||||
hasattr(self.__class__, "flaky_tests")
|
||||
and self._testMethodName in self.__class__.flaky_tests
|
||||
)
|
||||
|
||||
def get_test_tools(self):
|
||||
"""Get the test tools for function calling"""
|
||||
return [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "use this to get latest weather information for a city given its name",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "name of the city to get weather for",
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
},
|
||||
},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_pokemon_info",
|
||||
"description": "get detailed information about a pokemon given its name",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "name of the pokemon to get info for",
|
||||
}
|
||||
},
|
||||
"required": ["name"],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "make_next_step_decision",
|
||||
"description": "You will be given a trace of thinking process in the following format.\n\nQuestion: the input question you must answer\nTOOL: think about what to do, and choose a tool to use ONLY IF there are defined tools. \n You should never call the same tool with the same input twice in a row.\n If the previous conversation history already contains the information that can be retrieved from the tool, you should not call the tool again.\nOBSERVATION: the result of the tool call, NEVER include this in your response, this information will be provided\n... (this TOOL/OBSERVATION can repeat N times)\nANSWER: If you know the answer to the original question, require for more information,\n or you don't know the answer and there are no defined tools or all available tools are not helpful, respond with the answer without mentioning anything else.\n If the previous conversation history already contains the answer, respond with the answer right away.\n\n If no tools are configured, naturally mention this limitation while still being helpful. Briefly note that adding tools in the agent configuration would expand capabilities.\n\nYour task is to respond with the next step to take, based on the traces, \nor answer the question if you have enough information.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"decision": {
|
||||
"type": "string",
|
||||
"description": 'The next step to take, it must be either "TOOL" or "ANSWER". If the previous conversation history already contains the information that can be retrieved from the tool, you should not call the tool again. If there are no defined tools, you should not return "TOOL" in your response.',
|
||||
},
|
||||
"content": {
|
||||
"type": "string",
|
||||
"description": 'The content of the next step. If the decision is "TOOL", this should be a short and concise reasoning of why you chose the tool, MUST include the tool name. If the decision is "ANSWER", this should be the answer to the question. If no tools are available, integrate this limitation conversationally without sounding scripted.',
|
||||
},
|
||||
},
|
||||
"required": ["decision", "content"],
|
||||
},
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
def get_test_messages(self):
|
||||
"""Get test messages that should trigger tool usage"""
|
||||
return [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Answer the following questions as best you can:\n\nYou will be given a trace of thinking process in the following format.\n\nQuestion: the input question you must answer\nTOOL: think about what to do, and choose a tool to use ONLY IF there are defined tools\nOBSERVATION: the result of the tool call or the observation of the current task, NEVER include this in your response, this information will be provided\n... (this TOOL/OBSERVATION can repeat N times)\nANSWER: If you know the answer to the original question, require for more information, \nif the previous conversation history already contains the answer, \nor you don't know the answer and there are no defined tools or all available tools are not helpful, respond with the answer without mentioning anything else.\nYou may use light Markdown formatting to improve clarity (e.g. lists, **bold**, *italics*), but keep it minimal and unobtrusive.\n\nYour task is to respond with the next step to take, based on the traces, \nor answer the question if you have enough information.\n\nQuestion: what is the weather in top 5 populated cities in the US in celsius?\n\nTraces:\n\n\nThese are some additional instructions that you should follow:",
|
||||
}
|
||||
]
|
||||
|
||||
def get_travel_tools(self):
|
||||
"""Get tools for travel assistant scenario that should trigger multiple tool calls"""
|
||||
return [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather for a given location.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The name of the city or location.",
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_tourist_attractions",
|
||||
"description": "Get a list of top tourist attractions for a given city.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "The name of the city to find attractions for.",
|
||||
}
|
||||
},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
def get_travel_messages(self):
|
||||
"""Get travel assistant messages that should trigger multiple tool calls"""
|
||||
return [
|
||||
{
|
||||
"content": "You are a travel assistant providing real-time weather updates and top tourist attractions.",
|
||||
"role": "system",
|
||||
},
|
||||
{
|
||||
"content": "I'm planning a trip to Tokyo next week. What's the weather like? What are the most amazing sights?",
|
||||
"role": "user",
|
||||
},
|
||||
]
|
||||
|
||||
def test_tool_choice_auto_non_streaming(self):
|
||||
"""Test tool_choice='auto' in non-streaming mode"""
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=tools,
|
||||
tool_choice="auto",
|
||||
stream=False,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(response.choices[0].message)
|
||||
# With auto, tool calls are optional
|
||||
|
||||
def test_tool_choice_auto_streaming(self):
|
||||
"""Test tool_choice='auto' in streaming mode"""
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=tools,
|
||||
tool_choice="auto",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streaming response
|
||||
content_chunks = []
|
||||
tool_call_chunks = []
|
||||
|
||||
for chunk in response:
|
||||
if chunk.choices[0].delta.content:
|
||||
content_chunks.append(chunk.choices[0].delta.content)
|
||||
elif chunk.choices[0].delta.tool_calls:
|
||||
tool_call_chunks.extend(chunk.choices[0].delta.tool_calls)
|
||||
|
||||
# Should complete without errors
|
||||
self.assertIsInstance(content_chunks, list)
|
||||
self.assertIsInstance(tool_call_chunks, list)
|
||||
|
||||
def test_tool_choice_required_non_streaming(self):
|
||||
"""Test tool_choice='required' in non-streaming mode"""
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
temperature=0.2,
|
||||
tools=tools,
|
||||
tool_choice="required",
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# With required, we should get tool calls
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
self.assertIsNotNone(tool_calls)
|
||||
self.assertGreater(len(tool_calls), 0)
|
||||
|
||||
def test_tool_choice_required_streaming(self):
|
||||
"""Test tool_choice='required' in streaming mode"""
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=tools,
|
||||
tool_choice="required",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streaming response
|
||||
tool_call_chunks = []
|
||||
|
||||
for chunk in response:
|
||||
if chunk.choices[0].delta.tool_calls:
|
||||
tool_call_chunks.extend(chunk.choices[0].delta.tool_calls)
|
||||
|
||||
# With required, we should get tool call chunks
|
||||
self.assertGreater(len(tool_call_chunks), 0)
|
||||
|
||||
def test_tool_choice_specific_function_non_streaming(self):
|
||||
"""Test tool_choice with specific function in non-streaming mode"""
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
tool_choice = {"type": "function", "function": {"name": "get_weather"}}
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=tools,
|
||||
tool_choice=tool_choice,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Should call the specific function
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
self.assertIsNotNone(tool_calls)
|
||||
# Our messages ask the top 5 populated cities in the US, so the model could get 5 tool calls
|
||||
self.assertGreaterEqual(len(tool_calls), 1)
|
||||
for tool_call in tool_calls:
|
||||
self.assertEqual(tool_call.function.name, "get_weather")
|
||||
|
||||
def test_tool_choice_specific_function_streaming(self):
|
||||
"""Test tool_choice with specific function in streaming mode"""
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
tool_choice = {"type": "function", "function": {"name": "get_weather"}}
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=tools,
|
||||
tool_choice=tool_choice,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streaming response
|
||||
tool_call_chunks = []
|
||||
|
||||
for chunk in response:
|
||||
if chunk.choices[0].delta.tool_calls:
|
||||
tool_call_chunks.extend(chunk.choices[0].delta.tool_calls)
|
||||
|
||||
# Should get tool call chunks for the specific function
|
||||
self.assertGreater(len(tool_call_chunks), 0)
|
||||
|
||||
# Find function name in chunks
|
||||
found_name = None
|
||||
for chunk in tool_call_chunks:
|
||||
if chunk.function and chunk.function.name:
|
||||
found_name = chunk.function.name
|
||||
break
|
||||
|
||||
self.assertEqual(found_name, "get_weather")
|
||||
|
||||
def test_multi_tool_scenario_auto(self):
|
||||
"""Test multi-tool scenario with tool_choice='auto'"""
|
||||
tools = self.get_travel_tools()
|
||||
messages = self.get_travel_messages()
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
temperature=0.2,
|
||||
tools=tools,
|
||||
tool_choice="auto",
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Should complete without errors
|
||||
self.assertIsNotNone(response.choices[0].message)
|
||||
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
expected_functions = {"get_weather", "get_tourist_attractions"}
|
||||
|
||||
if self._is_flaky_test():
|
||||
# For flaky tests, just verify all called functions are available tools
|
||||
if tool_calls:
|
||||
available_names = [tool["function"]["name"] for tool in tools]
|
||||
for call in tool_calls:
|
||||
self.assertIn(call.function.name, available_names)
|
||||
else:
|
||||
# For non-flaky tests, enforce strict requirements
|
||||
self.assertIsNotNone(tool_calls, "Expected tool calls but got none")
|
||||
self.assertEqual(
|
||||
len(tool_calls), 2, f"Expected 2 tool calls, got {len(tool_calls)}"
|
||||
)
|
||||
|
||||
called_functions = {call.function.name for call in tool_calls}
|
||||
self.assertEqual(
|
||||
called_functions,
|
||||
expected_functions,
|
||||
f"Expected functions {expected_functions}, got {called_functions}",
|
||||
)
|
||||
|
||||
def test_multi_tool_scenario_required(self):
|
||||
"""Test multi-tool scenario with tool_choice='required'"""
|
||||
tools = self.get_travel_tools()
|
||||
messages = self.get_travel_messages()
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
temperature=0.2,
|
||||
tools=tools,
|
||||
tool_choice="required",
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# With required, we should get at least one tool call
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
self.assertIsNotNone(tool_calls)
|
||||
self.assertGreater(len(tool_calls), 0)
|
||||
|
||||
# Verify all called functions are available tools
|
||||
available_names = [tool["function"]["name"] for tool in tools]
|
||||
expected_functions = {"get_weather", "get_tourist_attractions"}
|
||||
|
||||
if self._is_flaky_test():
|
||||
# For flaky tests, just ensure basic functionality works
|
||||
self.assertGreater(
|
||||
len(tool_calls),
|
||||
0,
|
||||
f"Expected at least 1 tool call, got {len(tool_calls)}",
|
||||
)
|
||||
for call in tool_calls:
|
||||
self.assertIn(call.function.name, available_names)
|
||||
else:
|
||||
# For non-flaky tests, enforce strict requirements
|
||||
self.assertEqual(
|
||||
len(tool_calls), 2, f"Expected 2 tool calls, got {len(tool_calls)}"
|
||||
)
|
||||
|
||||
called_functions = {call.function.name for call in tool_calls}
|
||||
self.assertEqual(
|
||||
called_functions,
|
||||
expected_functions,
|
||||
f"Expected functions {expected_functions}, got {called_functions}",
|
||||
)
|
||||
|
||||
def test_error_handling_invalid_tool_choice(self):
|
||||
"""Test error handling for invalid tool_choice"""
|
||||
import logging
|
||||
from unittest.mock import patch
|
||||
|
||||
tools = self.get_test_tools()
|
||||
messages = self.get_test_messages()
|
||||
|
||||
# Test with invalid function name
|
||||
tool_choice = {"type": "function", "function": {"name": "nonexistent_function"}}
|
||||
|
||||
# The behavior could be either:
|
||||
# 1. Log a warning and continue (if fallback is implemented)
|
||||
# 2. Raise an exception (if strict validation is implemented)
|
||||
|
||||
# First try to capture any logging that might happen
|
||||
with patch("logging.warning") as mock_warning:
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=tools,
|
||||
tool_choice=tool_choice,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(response.choices[0].message)
|
||||
|
||||
if mock_warning.called:
|
||||
warning_message = mock_warning.call_args[0][0]
|
||||
self.assertIn("nonexistent_function", warning_message)
|
||||
|
||||
|
||||
class TestToolChoiceQwen25(TestToolChoiceLlama32):
|
||||
"""Test tool_choice functionality with Qwen2.5 model"""
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.flaky_tests = {}
|
||||
|
||||
cls.model = "Qwen/Qwen2.5-7B-Instruct"
|
||||
cls.base_url = DEFAULT_URL_FOR_TEST
|
||||
cls.api_key = "sk-123456"
|
||||
|
||||
cls.process = popen_launch_server(
|
||||
cls.model,
|
||||
cls.base_url,
|
||||
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
api_key=cls.api_key,
|
||||
other_args=[
|
||||
"--tool-call-parser",
|
||||
"qwen25",
|
||||
],
|
||||
)
|
||||
cls.base_url += "/v1"
|
||||
cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
|
||||
class TestToolChoiceMistral(TestToolChoiceLlama32):
|
||||
"""Test tool_choice functionality with Mistral model"""
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
# Mark flaky tests for this model
|
||||
cls.flaky_tests = {
|
||||
"test_multi_tool_scenario_auto",
|
||||
"test_multi_tool_scenario_required",
|
||||
}
|
||||
|
||||
cls.model = "mistralai/Mistral-7B-Instruct-v0.3"
|
||||
cls.base_url = DEFAULT_URL_FOR_TEST
|
||||
cls.api_key = "sk-123456"
|
||||
|
||||
cls.process = popen_launch_server(
|
||||
cls.model,
|
||||
cls.base_url,
|
||||
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
api_key=cls.api_key,
|
||||
other_args=[
|
||||
"--tool-call-parser",
|
||||
"mistral",
|
||||
],
|
||||
)
|
||||
cls.base_url += "/v1"
|
||||
cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
|
||||
# Skip for ci test
|
||||
# class TestToolChoiceGLM45(TestToolChoiceLlama32):
|
||||
# @classmethod
|
||||
# def setUpClass(cls):
|
||||
# # Replace with the model name needed for testing; if not required, reuse DEFAULT_SMALL_MODEL_NAME_FOR_TEST
|
||||
# cls.model = "THUDM/GLM-4.5"
|
||||
# cls.base_url = DEFAULT_URL_FOR_TEST
|
||||
# cls.api_key = "sk-123456"
|
||||
|
||||
# # Start the local OpenAI Server. If necessary, you can add other parameters such as --enable-tools.
|
||||
# cls.process = popen_launch_server(
|
||||
# cls.model,
|
||||
# cls.base_url,
|
||||
# timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
# api_key=cls.api_key,
|
||||
# other_args=[
|
||||
# # If your server needs extra parameters to test function calling, please add them here.
|
||||
# "--tool-call-parser",
|
||||
# "glm45",
|
||||
# "--reasoning-parser",
|
||||
# "glm45",
|
||||
# "--tp-size",
|
||||
# "8"
|
||||
# ],
|
||||
# )
|
||||
# cls.base_url += "/v1"
|
||||
# cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user