feat(Tool Calling): Support required and specific function mode (#6550)
This commit is contained in:
Chang Su
576
test/srt/test_openai_function_calling.py
Normal file
576
test/srt/test_openai_function_calling.py
Normal file
@@ -0,0 +1,576 @@
|
||||
import json
|
||||
import time
|
||||
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_SMALL_MODEL_NAME_FOR_TEST,
|
||||
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
|
||||
DEFAULT_URL_FOR_TEST,
|
||||
CustomTestCase,
|
||||
popen_launch_server,
|
||||
)
|
||||
|
||||
|
||||
class TestOpenAIServerFunctionCalling(CustomTestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
# Replace with the model name needed for testing; if not required, reuse DEFAULT_SMALL_MODEL_NAME_FOR_TEST
|
||||
cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
|
||||
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",
|
||||
"llama3",
|
||||
],
|
||||
)
|
||||
cls.base_url += "/v1"
|
||||
cls.tokenizer = get_tokenizer(cls.model)
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
kill_process_tree(cls.process.pid)
|
||||
|
||||
def test_function_calling_format(self):
|
||||
"""
|
||||
Test: Whether the function call format returned by the AI is correct.
|
||||
When returning a tool call, message.content should be None, and tool_calls should be a list.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "add",
|
||||
"description": "Compute the sum of two numbers",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"a": {
|
||||
"type": "int",
|
||||
"description": "A number",
|
||||
},
|
||||
"b": {
|
||||
"type": "int",
|
||||
"description": "A number",
|
||||
},
|
||||
},
|
||||
"required": ["a", "b"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
messages = [{"role": "user", "content": "Compute (3+5)"}]
|
||||
response = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=False,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
|
||||
assert (
|
||||
isinstance(tool_calls, list) and len(tool_calls) > 0
|
||||
), "tool_calls should be a non-empty list"
|
||||
|
||||
function_name = tool_calls[0].function.name
|
||||
assert function_name == "add", "Function name should be 'add'"
|
||||
|
||||
def test_function_calling_streaming_simple(self):
|
||||
"""
|
||||
Test: Whether the function name can be correctly recognized in streaming mode.
|
||||
- Expect a function call to be found, and the function name to be correct.
|
||||
- Verify that streaming mode returns at least multiple chunks.
|
||||
"""
|
||||
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": "What is the temperature in Paris?"}]
|
||||
|
||||
response_stream = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=True,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
chunks = list(response_stream)
|
||||
self.assertTrue(len(chunks) > 0, "Streaming should return at least one chunk")
|
||||
|
||||
found_function_name = False
|
||||
for chunk in chunks:
|
||||
choice = chunk.choices[0]
|
||||
# Check whether the current chunk contains tool_calls
|
||||
if choice.delta.tool_calls:
|
||||
tool_call = choice.delta.tool_calls[0]
|
||||
if tool_call.function.name:
|
||||
self.assertEqual(
|
||||
tool_call.function.name,
|
||||
"get_current_weather",
|
||||
"Function name should be 'get_current_weather'",
|
||||
)
|
||||
found_function_name = True
|
||||
break
|
||||
|
||||
self.assertTrue(
|
||||
found_function_name,
|
||||
"Target function name 'get_current_weather' was not found in the streaming chunks",
|
||||
)
|
||||
|
||||
def test_function_calling_streaming_args_parsing(self):
|
||||
"""
|
||||
Test: Whether the function call arguments returned in streaming mode can be correctly concatenated into valid JSON.
|
||||
- The user request requires multiple parameters.
|
||||
- AI may return the arguments in chunks that need to be concatenated.
|
||||
"""
|
||||
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "add",
|
||||
"description": "Compute the sum of two integers",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"a": {
|
||||
"type": "integer",
|
||||
"description": "First integer",
|
||||
},
|
||||
"b": {
|
||||
"type": "integer",
|
||||
"description": "Second integer",
|
||||
},
|
||||
},
|
||||
"required": ["a", "b"],
|
||||
},
|
||||
"strict": True, # Llama-3.2-1B is flaky in tool call. It won't always respond with parameters unless we set strict.
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "Please sum 5 and 7, just call the function."}
|
||||
]
|
||||
|
||||
response_stream = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.9,
|
||||
top_p=0.9,
|
||||
stream=True,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
argument_fragments = []
|
||||
function_name = None
|
||||
for chunk in response_stream:
|
||||
choice = chunk.choices[0]
|
||||
if choice.delta.tool_calls:
|
||||
tool_call = choice.delta.tool_calls[0]
|
||||
# Record the function name on first occurrence
|
||||
function_name = tool_call.function.name or function_name
|
||||
# In case of multiple chunks, JSON fragments may need to be concatenated
|
||||
if tool_call.function.arguments is not None:
|
||||
argument_fragments.append(tool_call.function.arguments)
|
||||
|
||||
self.assertEqual(function_name, "add", "Function name should be 'add'")
|
||||
joined_args = "".join(argument_fragments)
|
||||
self.assertTrue(
|
||||
len(joined_args) > 0,
|
||||
"No parameter fragments were returned in the function call",
|
||||
)
|
||||
|
||||
# Check whether the concatenated JSON is valid
|
||||
try:
|
||||
args_obj = json.loads(joined_args)
|
||||
except json.JSONDecodeError:
|
||||
self.fail(
|
||||
"The concatenated tool call arguments are not valid JSON, parsing failed"
|
||||
)
|
||||
|
||||
self.assertIn("a", args_obj, "Missing parameter 'a'")
|
||||
self.assertIn("b", args_obj, "Missing parameter 'b'")
|
||||
self.assertEqual(str(args_obj["a"]), "5", "Parameter a should be 5")
|
||||
self.assertEqual(str(args_obj["b"]), "7", "Parameter b should be 7")
|
||||
|
||||
def test_function_call_strict(self):
|
||||
"""
|
||||
Test: Whether the strict mode of function calling works as expected.
|
||||
- When strict mode is enabled, the AI should not return a function call if the function name is not recognized.
|
||||
"""
|
||||
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,
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "Please compute 5 - 7, using your tool."}
|
||||
]
|
||||
response = client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
stream=False,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
tool_calls = response.choices[0].message.tool_calls
|
||||
function_name = tool_calls[0].function.name
|
||||
arguments = tool_calls[0].function.arguments
|
||||
args_obj = json.loads(arguments)
|
||||
|
||||
self.assertEqual(function_name, "sub", "Function name should be 'sub'")
|
||||
self.assertEqual(str(args_obj["int_a"]), "5", "Parameter int_a should be 5")
|
||||
self.assertEqual(str(args_obj["int_b"]), "7", "Parameter int_b should be 7")
|
||||
|
||||
def test_function_call_required(self):
|
||||
"""
|
||||
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,
|
||||
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", "Function name should be 'get_weather'"
|
||||
)
|
||||
self.assertIn("city", args_obj, "Function arguments should have 'city'")
|
||||
self.assertIn(
|
||||
"Paris", args_obj["city"], "Parameter city should contain 'Paris'"
|
||||
) # might be flaky
|
||||
|
||||
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,
|
||||
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'")
|
||||
|
||||
|
||||
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'",
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user