713 lines
28 KiB
Python
713 lines
28 KiB
Python
# Copyright 2023-2024 SGLang Team
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Tests for OpenAI API protocol models"""
|
|
|
|
import unittest
|
|
from typing import List, Optional
|
|
|
|
from pydantic import BaseModel, Field, ValidationError
|
|
|
|
from sglang.srt.entrypoints.openai.protocol import (
|
|
ChatCompletionMessageContentImageURL,
|
|
ChatCompletionRequest,
|
|
ChatCompletionResponse,
|
|
ChatCompletionResponseChoice,
|
|
ChatMessage,
|
|
CompletionRequest,
|
|
Function,
|
|
ModelCard,
|
|
ModelList,
|
|
Tool,
|
|
UsageInfo,
|
|
)
|
|
from sglang.test.ci.ci_register import register_cpu_ci
|
|
|
|
register_cpu_ci(est_time=7, suite="base-a-test-cpu")
|
|
|
|
|
|
class TestModelCard(unittest.TestCase):
|
|
"""Test ModelCard protocol model"""
|
|
|
|
def test_model_card_serialization(self):
|
|
"""Test model card JSON serialization"""
|
|
card = ModelCard(id="test-model", max_model_len=4096)
|
|
data = card.model_dump()
|
|
self.assertEqual(data["id"], "test-model")
|
|
self.assertEqual(data["object"], "model")
|
|
self.assertEqual(data["max_model_len"], 4096)
|
|
|
|
|
|
class TestModelList(unittest.TestCase):
|
|
"""Test ModelList protocol model"""
|
|
|
|
def test_empty_model_list(self):
|
|
"""Test empty model list creation"""
|
|
model_list = ModelList()
|
|
self.assertEqual(model_list.object, "list")
|
|
self.assertEqual(len(model_list.data), 0)
|
|
|
|
def test_model_list_with_cards(self):
|
|
"""Test model list with model cards"""
|
|
cards = [
|
|
ModelCard(id="model-1"),
|
|
ModelCard(id="model-2", max_model_len=2048),
|
|
]
|
|
model_list = ModelList(data=cards)
|
|
self.assertEqual(len(model_list.data), 2)
|
|
self.assertEqual(model_list.data[0].id, "model-1")
|
|
self.assertEqual(model_list.data[1].id, "model-2")
|
|
|
|
|
|
class TestCompletionRequest(unittest.TestCase):
|
|
"""Test CompletionRequest protocol model"""
|
|
|
|
def test_basic_completion_request(self):
|
|
"""Test basic completion request"""
|
|
request = CompletionRequest(model="test-model", prompt="Hello world")
|
|
self.assertEqual(request.model, "test-model")
|
|
self.assertEqual(request.prompt, "Hello world")
|
|
self.assertEqual(request.max_tokens, 16) # default
|
|
self.assertEqual(request.temperature, 1.0) # default
|
|
self.assertEqual(request.n, 1) # default
|
|
self.assertFalse(request.stream) # default
|
|
self.assertFalse(request.echo) # default
|
|
|
|
def test_completion_request_sglang_extensions(self):
|
|
"""Test completion request with SGLang-specific extensions"""
|
|
request = CompletionRequest(
|
|
model="test-model",
|
|
prompt="Hello",
|
|
top_k=50,
|
|
min_p=0.1,
|
|
repetition_penalty=1.1,
|
|
regex=r"\d+",
|
|
json_schema='{"type": "object"}',
|
|
lora_path="/path/to/lora",
|
|
)
|
|
self.assertEqual(request.top_k, 50)
|
|
self.assertEqual(request.min_p, 0.1)
|
|
self.assertEqual(request.repetition_penalty, 1.1)
|
|
self.assertEqual(request.regex, r"\d+")
|
|
self.assertEqual(request.json_schema, '{"type": "object"}')
|
|
self.assertEqual(request.lora_path, "/path/to/lora")
|
|
|
|
def test_completion_request_validation_errors(self):
|
|
"""Test completion request validation errors"""
|
|
with self.assertRaises(ValidationError):
|
|
CompletionRequest() # missing required fields
|
|
|
|
with self.assertRaises(ValidationError):
|
|
CompletionRequest(model="test-model") # missing prompt
|
|
|
|
|
|
class TestChatCompletionRequest(unittest.TestCase):
|
|
"""Test ChatCompletionRequest protocol model"""
|
|
|
|
def test_json_schema_strict_requires_json_boolean(self):
|
|
base_request = {
|
|
"model": "test-model",
|
|
"messages": [{"role": "user", "content": "Hello"}],
|
|
"response_format": {
|
|
"type": "json_schema",
|
|
"json_schema": {
|
|
"name": "answer",
|
|
"schema": {"type": "object"},
|
|
},
|
|
},
|
|
}
|
|
|
|
for strict in (True, False, None):
|
|
with self.subTest(strict=strict):
|
|
response_format = dict(base_request["response_format"])
|
|
response_format["json_schema"] = {
|
|
**response_format["json_schema"],
|
|
"strict": strict,
|
|
}
|
|
request = ChatCompletionRequest.model_validate(
|
|
{**base_request, "response_format": response_format}
|
|
)
|
|
self.assertIs(request.response_format.json_schema.strict, strict)
|
|
|
|
for strict in ("yes", "false", 0, 1):
|
|
with self.subTest(strict=strict), self.assertRaises(ValidationError):
|
|
response_format = dict(base_request["response_format"])
|
|
response_format["json_schema"] = {
|
|
**response_format["json_schema"],
|
|
"strict": strict,
|
|
}
|
|
ChatCompletionRequest.model_validate(
|
|
{**base_request, "response_format": response_format}
|
|
)
|
|
|
|
def test_basic_chat_completion_request(self):
|
|
"""Test basic chat completion request"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(model="test-model", messages=messages)
|
|
self.assertEqual(request.model, "test-model")
|
|
self.assertEqual(len(request.messages), 1)
|
|
self.assertEqual(request.messages[0].role, "user")
|
|
self.assertEqual(request.messages[0].content, "Hello")
|
|
self.assertEqual(request.temperature, None) # default
|
|
self.assertFalse(request.stream) # default
|
|
self.assertFalse(request.return_sampling_mask)
|
|
self.assertEqual(request.tool_choice, "none") # default when no tools
|
|
|
|
def test_image_content_hash_validation(self):
|
|
digest = "sha256:" + "AB" * 32
|
|
image = ChatCompletionMessageContentImageURL(
|
|
url="https://example.com/image.jpg", content_hash=digest
|
|
)
|
|
self.assertEqual(image.content_hash, digest.lower())
|
|
with self.assertRaises(ValidationError):
|
|
ChatCompletionMessageContentImageURL(
|
|
url="https://example.com/image.jpg", content_hash="not-a-hash"
|
|
)
|
|
|
|
def test_sampling_param_build(self):
|
|
req = ChatCompletionRequest(
|
|
model="x",
|
|
messages=[{"role": "user", "content": "Hi"}],
|
|
temperature=0.8,
|
|
max_tokens=150,
|
|
min_tokens=5,
|
|
top_p=0.9,
|
|
stop=["</s>"],
|
|
)
|
|
params = req.to_sampling_params(["</s>"], {}, None)
|
|
self.assertEqual(params["temperature"], 0.8)
|
|
self.assertEqual(params["max_new_tokens"], 150)
|
|
self.assertEqual(params["min_new_tokens"], 5)
|
|
self.assertEqual(params["stop"], ["</s>"])
|
|
|
|
def test_chat_completion_tool_choice_validation(self):
|
|
"""Test tool choice validation logic"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
|
|
# No tools, tool_choice should default to "none"
|
|
request1 = ChatCompletionRequest(model="test-model", messages=messages)
|
|
self.assertEqual(request1.tool_choice, "none")
|
|
|
|
# With tools, tool_choice should default to "auto"
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {"name": "test_func", "description": "Test function"},
|
|
}
|
|
]
|
|
request2 = ChatCompletionRequest(
|
|
model="test-model", messages=messages, tools=tools
|
|
)
|
|
self.assertEqual(request2.tool_choice, "auto")
|
|
|
|
def test_chat_completion_sglang_extensions(self):
|
|
"""Test chat completion with SGLang extensions"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
top_k=40,
|
|
min_p=0.05,
|
|
separate_reasoning=False,
|
|
stream_reasoning=False,
|
|
chat_template_kwargs={"custom_param": "value"},
|
|
)
|
|
self.assertEqual(request.top_k, 40)
|
|
self.assertEqual(request.min_p, 0.05)
|
|
self.assertFalse(request.separate_reasoning)
|
|
self.assertFalse(request.stream_reasoning)
|
|
self.assertEqual(request.chat_template_kwargs, {"custom_param": "value"})
|
|
|
|
def test_chat_completion_tito_extensions(self):
|
|
"""Test chat completion with pre-tokenized prompt extensions."""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
input_ids=[101, 102, 103],
|
|
return_prompt_token_ids=True,
|
|
return_meta_info=True,
|
|
)
|
|
self.assertEqual(request.input_ids, [101, 102, 103])
|
|
self.assertTrue(request.return_prompt_token_ids)
|
|
self.assertTrue(request.return_meta_info)
|
|
|
|
def test_chat_completion_reasoning_effort(self):
|
|
"""Test chat completion with reasoning effort"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning={
|
|
"enabled": True,
|
|
"reasoning_effort": "high",
|
|
},
|
|
)
|
|
self.assertEqual(request.reasoning_effort, "high")
|
|
self.assertEqual(
|
|
request.chat_template_kwargs,
|
|
{"thinking": True, "enable_thinking": True},
|
|
)
|
|
|
|
def test_chat_completion_reasoning_effort_high_enables_thinking(self):
|
|
"""Top-level reasoning_effort='high' enables thinking."""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning_effort="high",
|
|
)
|
|
self.assertEqual(request.reasoning_effort, "high")
|
|
self.assertEqual(
|
|
request.chat_template_kwargs,
|
|
{"thinking": True, "enable_thinking": True},
|
|
)
|
|
|
|
def test_chat_completion_reasoning_effort_none(self):
|
|
"""Test reasoning_effort='none' disables thinking"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning_effort="none",
|
|
)
|
|
self.assertEqual(request.reasoning_effort, "none")
|
|
self.assertFalse(request.chat_template_kwargs.get("thinking"))
|
|
self.assertFalse(request.chat_template_kwargs.get("enable_thinking"))
|
|
|
|
def test_chat_completion_reasoning_effort_none_from_reasoning_dict(self):
|
|
"""Test reasoning_effort='none' via nested reasoning dict"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning={"effort": "none"},
|
|
)
|
|
self.assertEqual(request.reasoning_effort, "none")
|
|
self.assertFalse(request.chat_template_kwargs.get("thinking"))
|
|
self.assertFalse(request.chat_template_kwargs.get("enable_thinking"))
|
|
|
|
def test_chat_completion_reasoning_effort_none_overrides_enabled(self):
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning={"enabled": True, "effort": "none"},
|
|
)
|
|
self.assertEqual(request.reasoning_effort, "none")
|
|
self.assertFalse(request.chat_template_kwargs.get("thinking"))
|
|
self.assertFalse(request.chat_template_kwargs.get("enable_thinking"))
|
|
|
|
def test_chat_completion_extended_reasoning_effort_levels(self):
|
|
"""Extended effort levels work in both supported request forms."""
|
|
from pydantic import ValidationError
|
|
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
for effort in ("xhigh", "max"):
|
|
with self.subTest(effort=effort, request_form="top-level"):
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning_effort=effort,
|
|
)
|
|
self.assertEqual(request.reasoning_effort, effort)
|
|
|
|
with self.subTest(effort=effort, request_form="nested"):
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning={"effort": effort},
|
|
)
|
|
self.assertEqual(request.reasoning_effort, effort)
|
|
|
|
# Unknown values still rejected.
|
|
with self.assertRaises(ValidationError):
|
|
ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
reasoning_effort="ultra",
|
|
)
|
|
|
|
def test_chat_completion_reasoning_effort_is_strictly_validated(self):
|
|
from pydantic import ValidationError
|
|
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
for request_kwargs, expected in (
|
|
({"reasoning_effort": 0.99}, 0.99),
|
|
({"reasoning": {"effort": 0.0}}, 0.0),
|
|
# numeric strings coerce identically on BOTH request surfaces
|
|
# (the top-level field's lax union already coerced them).
|
|
({"reasoning": {"effort": "0.5"}}, 0.5),
|
|
({"reasoning": {"effort": None, "reasoning_effort": 0.4}}, 0.4),
|
|
):
|
|
request = ChatCompletionRequest(
|
|
model="test-model", messages=messages, **request_kwargs
|
|
)
|
|
self.assertEqual(request.reasoning_effort, expected)
|
|
|
|
for request_kwargs in (
|
|
{"reasoning_effort": -0.1},
|
|
# 0.99 is the maximum valid effort; 1.0 is out of range.
|
|
{"reasoning_effort": 1.0},
|
|
{"reasoning_effort": 1.1},
|
|
{"reasoning_effort": float("nan")},
|
|
{"reasoning_effort": True},
|
|
{"reasoning": {"effort": "invalid"}},
|
|
{"reasoning": {"effort": 1.0}},
|
|
{"reasoning": {"effort": 1.1}},
|
|
{"reasoning": {"effort": "1.5"}},
|
|
):
|
|
with self.subTest(request_kwargs=request_kwargs), self.assertRaises(
|
|
ValidationError
|
|
):
|
|
ChatCompletionRequest(
|
|
model="test-model", messages=messages, **request_kwargs
|
|
)
|
|
|
|
def test_chat_completion_accepts_ordered_thinking_parts(self):
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=[
|
|
{
|
|
"role": "assistant",
|
|
"content": [
|
|
{"type": "thinking", "thinking": "first"},
|
|
{"type": "text", "text": "visible"},
|
|
{"type": "reasoning", "text": "second"},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
parts = request.messages[0].content
|
|
self.assertEqual(
|
|
[part.type for part in parts], ["thinking", "text", "reasoning"]
|
|
)
|
|
|
|
def test_chat_completion_rejects_thinking_parts_outside_assistant(self):
|
|
"""Bug regression: adding the thinking part to the SHARED content-part
|
|
union silently widened acceptance to every role (user/system/tool) and
|
|
every model family, where downstream templates cannot render it —
|
|
replacing the previous clean 422 with template-dependent behavior."""
|
|
from pydantic import ValidationError
|
|
|
|
for role in ("user", "system", "tool"):
|
|
with self.subTest(role=role), self.assertRaises(ValidationError):
|
|
ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=[
|
|
{
|
|
"role": role,
|
|
"content": [{"type": "thinking", "thinking": "x"}],
|
|
}
|
|
],
|
|
)
|
|
|
|
def test_chat_completion_json_format(self):
|
|
"""Test chat completion json format"""
|
|
transcript = "Good morning! It's 7:00 AM, and I'm just waking up. Today is going to be a busy day, "
|
|
"so let's get started. First, I need to make a quick breakfast. I think I'll have some "
|
|
"scrambled eggs and toast with a cup of coffee. While I'm cooking, I'll also check my "
|
|
"emails to see if there's anything urgent."
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": "The following is a voice message transcript. Only answer in JSON.",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": transcript,
|
|
},
|
|
]
|
|
|
|
class VoiceNote(BaseModel):
|
|
title: str = Field(description="A title for the voice note")
|
|
summary: str = Field(
|
|
description="A short one sentence summary of the voice note."
|
|
)
|
|
strict: Optional[bool] = True
|
|
actionItems: List[str] = Field(
|
|
description="A list of action items from the voice note"
|
|
)
|
|
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
top_k=40,
|
|
min_p=0.05,
|
|
separate_reasoning=False,
|
|
stream_reasoning=False,
|
|
chat_template_kwargs={"custom_param": "value"},
|
|
response_format={
|
|
"type": "json_schema",
|
|
"schema": VoiceNote.model_json_schema(),
|
|
},
|
|
)
|
|
res_format = request.response_format
|
|
json_format = res_format.json_schema
|
|
name = json_format.name
|
|
schema = json_format.schema_
|
|
strict = json_format.strict
|
|
self.assertEqual(name, "VoiceNote")
|
|
self.assertEqual(strict, True)
|
|
self.assertNotIn("strict", schema["properties"])
|
|
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=messages,
|
|
top_k=40,
|
|
min_p=0.05,
|
|
separate_reasoning=False,
|
|
stream_reasoning=False,
|
|
chat_template_kwargs={"custom_param": "value"},
|
|
response_format={
|
|
"type": "json_schema",
|
|
"json_schema": {
|
|
"name": "VoiceNote",
|
|
"schema": VoiceNote.model_json_schema(),
|
|
"strict": True,
|
|
},
|
|
},
|
|
)
|
|
res_format = request.response_format
|
|
json_format = res_format.json_schema
|
|
name = json_format.name
|
|
schema = json_format.schema_
|
|
strict = json_format.strict
|
|
self.assertEqual(name, "VoiceNote")
|
|
self.assertEqual(strict, True)
|
|
|
|
def test_schema_derived_strict_false_constraint_gated_on_renderer(self):
|
|
"""A `strict` field on the user's model doubles as the protocol switch.
|
|
|
|
set_json_schema pops `strict` out of the schema's properties and feeds
|
|
its default into response_format. strict=False drops the sampling
|
|
constraint only when the renderer forwards response_format to the
|
|
model; otherwise the schema would be silently ignored, so the
|
|
constraint stays installed.
|
|
"""
|
|
|
|
class Note(BaseModel):
|
|
title: str
|
|
strict: bool = False
|
|
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=[{"role": "user", "content": "Return JSON"}],
|
|
response_format={
|
|
"type": "json_schema",
|
|
"schema": Note.model_json_schema(),
|
|
},
|
|
)
|
|
|
|
self.assertIs(request.response_format.json_schema.strict, False)
|
|
self.assertNotIn(
|
|
"strict", request.response_format.json_schema.schema_["properties"]
|
|
)
|
|
sampling_params = request.to_sampling_params(
|
|
stop=[], model_generation_config={}
|
|
)
|
|
self.assertIn("json_schema", sampling_params)
|
|
sampling_params = request.to_sampling_params(
|
|
stop=[],
|
|
model_generation_config={},
|
|
renderer_handles_response_format=True,
|
|
)
|
|
self.assertNotIn("json_schema", sampling_params)
|
|
|
|
def test_non_strict_response_format_constraint_gated_on_renderer(self):
|
|
request = ChatCompletionRequest(
|
|
model="test-model",
|
|
messages=[{"role": "user", "content": "Return JSON"}],
|
|
response_format={
|
|
"type": "json_schema",
|
|
"json_schema": {
|
|
"name": "answer",
|
|
"schema": {"type": "object"},
|
|
"strict": False,
|
|
},
|
|
},
|
|
)
|
|
sampling_params = request.to_sampling_params(
|
|
stop=[], model_generation_config={}
|
|
)
|
|
self.assertIn("json_schema", sampling_params)
|
|
sampling_params = request.to_sampling_params(
|
|
stop=[],
|
|
model_generation_config={},
|
|
renderer_handles_response_format=True,
|
|
)
|
|
self.assertNotIn("json_schema", sampling_params)
|
|
|
|
|
|
class TestModelSerialization(unittest.TestCase):
|
|
"""Test model serialization with hidden states"""
|
|
|
|
def test_hidden_states_excluded_when_none(self):
|
|
"""Test that None hidden_states are excluded with exclude_none=True"""
|
|
choice = ChatCompletionResponseChoice(
|
|
index=0,
|
|
message=ChatMessage(role="assistant", content="Hello"),
|
|
finish_reason="stop",
|
|
hidden_states=None,
|
|
)
|
|
|
|
response = ChatCompletionResponse(
|
|
id="test-id",
|
|
model="test-model",
|
|
choices=[choice],
|
|
usage=UsageInfo(prompt_tokens=5, completion_tokens=1, total_tokens=6),
|
|
)
|
|
|
|
# Test exclude_none serialization (should exclude None hidden_states)
|
|
data = response.model_dump(exclude_none=True)
|
|
self.assertNotIn("hidden_states", data["choices"][0])
|
|
|
|
def test_hidden_states_included_when_not_none(self):
|
|
"""Test that non-None hidden_states are included"""
|
|
choice = ChatCompletionResponseChoice(
|
|
index=0,
|
|
message=ChatMessage(role="assistant", content="Hello"),
|
|
finish_reason="stop",
|
|
hidden_states=[0.1, 0.2, 0.3],
|
|
)
|
|
|
|
response = ChatCompletionResponse(
|
|
id="test-id",
|
|
model="test-model",
|
|
choices=[choice],
|
|
usage=UsageInfo(prompt_tokens=5, completion_tokens=1, total_tokens=6),
|
|
)
|
|
|
|
# Test exclude_none serialization (should include non-None hidden_states)
|
|
data = response.model_dump(exclude_none=True)
|
|
self.assertIn("hidden_states", data["choices"][0])
|
|
self.assertEqual(data["choices"][0]["hidden_states"], [0.1, 0.2, 0.3])
|
|
|
|
def test_prompt_token_ids_and_meta_info_serialization(self):
|
|
"""Test that prompt_token_ids and meta_info serialize only when set."""
|
|
default_choice = ChatCompletionResponseChoice(
|
|
index=0,
|
|
message=ChatMessage(role="assistant", content="Hello"),
|
|
finish_reason="stop",
|
|
)
|
|
default_data = default_choice.model_dump()
|
|
self.assertNotIn("prompt_token_ids", default_data)
|
|
self.assertNotIn("response_token_ids", default_data)
|
|
self.assertNotIn("meta_info", default_data)
|
|
|
|
choice = ChatCompletionResponseChoice(
|
|
index=0,
|
|
message=ChatMessage(role="assistant", content="Hello"),
|
|
finish_reason="stop",
|
|
prompt_token_ids=[1, 2, 3],
|
|
response_token_ids=[4, 5],
|
|
meta_info={"prompt_tokens": 3},
|
|
)
|
|
data = choice.model_dump()
|
|
self.assertEqual(data["prompt_token_ids"], [1, 2, 3])
|
|
self.assertNotIn("token_ids", data)
|
|
self.assertEqual(data["response_token_ids"], [4, 5])
|
|
self.assertEqual(data["meta_info"], {"prompt_tokens": 3})
|
|
|
|
|
|
class TestFunctionDeferLoading(unittest.TestCase):
|
|
"""Test defer_loading field behavior on Function/Tool."""
|
|
|
|
def test_function_defaults_preserve_strict(self):
|
|
"""strict must default to False and be present in dumps so downstream
|
|
code (function_call_parser, chat templates) sees the expected shape."""
|
|
f = Function(name="foo")
|
|
data = f.model_dump()
|
|
self.assertEqual(data["name"], "foo")
|
|
self.assertEqual(data["strict"], False)
|
|
self.assertNotIn("defer_loading", data)
|
|
|
|
def test_function_defer_loading_true_serialized(self):
|
|
f = Function(name="foo", defer_loading=True)
|
|
data = f.model_dump()
|
|
self.assertTrue(data["defer_loading"])
|
|
self.assertEqual(data["strict"], False)
|
|
|
|
def test_function_defer_loading_false_serialized(self):
|
|
"""defer_loading=False is an explicit value and must be preserved."""
|
|
f = Function(name="foo", defer_loading=False)
|
|
data = f.model_dump()
|
|
self.assertIn("defer_loading", data)
|
|
self.assertFalse(data["defer_loading"])
|
|
|
|
def test_tool_level_defer_loading_propagates_to_function(self):
|
|
"""defer_loading at the Tool level should propagate to Function."""
|
|
tool = Tool(
|
|
type="function",
|
|
defer_loading=True,
|
|
function={"name": "search_db"},
|
|
)
|
|
self.assertTrue(tool.function.defer_loading)
|
|
data = tool.model_dump()
|
|
self.assertTrue(data["function"]["defer_loading"])
|
|
|
|
def test_function_level_defer_loading_wins_over_tool_level(self):
|
|
"""Explicit function-level value is preserved when both set."""
|
|
tool = Tool(
|
|
type="function",
|
|
defer_loading=True,
|
|
function={"name": "search_db", "defer_loading": False},
|
|
)
|
|
self.assertFalse(tool.function.defer_loading)
|
|
|
|
def test_tool_reference_content_part_accepted(self):
|
|
"""Chat completion should accept tool_reference content on tool-role
|
|
messages (GLM-specific extension consumed by the chat template)."""
|
|
messages = [
|
|
{
|
|
"role": "tool",
|
|
"tool_call_id": "call_1",
|
|
"content": [
|
|
{"type": "tool_reference", "name": "search_db"},
|
|
{"type": "text", "text": "ok"},
|
|
],
|
|
},
|
|
]
|
|
request = ChatCompletionRequest(model="test-model", messages=messages)
|
|
parts = request.messages[0].content
|
|
self.assertEqual(len(parts), 2)
|
|
self.assertEqual(parts[0].type, "tool_reference")
|
|
self.assertEqual(parts[0].name, "search_db")
|
|
self.assertEqual(parts[1].type, "text")
|
|
|
|
|
|
class TestValidationEdgeCases(unittest.TestCase):
|
|
"""Test edge cases and validation scenarios"""
|
|
|
|
def test_invalid_tool_choice_type(self):
|
|
"""Test invalid tool choice type"""
|
|
messages = [{"role": "user", "content": "Hello"}]
|
|
with self.assertRaises(ValidationError):
|
|
ChatCompletionRequest(
|
|
model="test-model", messages=messages, tool_choice=123
|
|
)
|
|
|
|
def test_negative_token_limits(self):
|
|
"""Test negative token limits"""
|
|
with self.assertRaises(ValidationError):
|
|
CompletionRequest(model="test-model", prompt="Hello", max_tokens=-1)
|
|
|
|
|
|
class TestParsedResponseFieldsProtocol(unittest.TestCase):
|
|
"""Test ParsedResponseFields protocol."""
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main(verbosity=2)
|