vllm/tests/entrypoints/conftest.py
HAIAI aee76334d9
[amd_dev] branch rebase (#25753)
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211 lines
5.5 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
@pytest.fixture
def sample_prompts():
return [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
@pytest.fixture
def sample_token_ids():
return [
[0],
[0, 1],
[0, 2, 1],
[0, 3, 1, 2],
]
@pytest.fixture
def sample_regex():
return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
@pytest.fixture
def sample_json_schema():
return {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"age": {
"type": "integer"
},
"skills": {
"type": "array",
"items": {
"type": "string",
"maxLength": 10
},
"minItems": 3
},
"work_history": {
"type": "array",
"items": {
"type": "object",
"properties": {
"company": {
"type": "string"
},
"duration": {
"type": "number"
},
"position": {
"type": "string"
}
},
"required": ["company", "position"]
}
}
},
"required": ["name", "age", "skills", "work_history"]
}
@pytest.fixture
def sample_complex_json_schema():
return {
"type": "object",
"properties": {
"score": {
"type": "integer",
"minimum": 0,
"maximum": 100 # Numeric range
},
"grade": {
"type": "string",
"pattern": "^[A-D]$" # Regex pattern
},
"email": {
"type": "string",
"pattern": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
},
"tags": {
"type": "array",
"items": {
"type": "string",
"pattern":
"^[a-z]{1,10}$" # Combining length and pattern restrictions
}
}
},
"required": ["score", "grade", "email", "tags"]
}
@pytest.fixture
def sample_definition_json_schema():
return {
'$defs': {
'Step': {
'properties': {
'explanation': {
'title': 'Explanation',
'type': 'string'
},
'output': {
'title': 'Output',
'type': 'string'
}
},
'required': ['explanation', 'output'],
'title': 'Step',
'type': 'object'
}
},
'properties': {
'steps': {
'items': {
'$ref': '#/$defs/Step'
},
'title': 'Steps',
'type': 'array'
},
'final_answer': {
'title': 'Final Answer',
'type': 'string'
}
},
'required': ['steps', 'final_answer'],
'title': 'MathReasoning',
'type': 'object'
}
@pytest.fixture
def sample_enum_json_schema():
return {
"type": "object",
"properties": {
"status": {
"type": "string",
"enum": ["active", "inactive",
"pending"] # Literal values using enum
},
"priority": {
"type": "string",
"enum": ["low", "medium", "high", "critical"]
},
"category": {
"type": "object",
"properties": {
"type": {
"type": "string",
"enum": ["bug", "feature", "improvement"]
},
"severity": {
"type": "integer",
"enum": [1, 2, 3, 4,
5] # Enum can also contain numbers
}
},
"required": ["type", "severity"]
},
"flags": {
"type": "array",
"items": {
"type": "string",
"enum": ["urgent", "blocked", "needs_review", "approved"]
}
}
},
"required": ["status", "priority", "category", "flags"]
}
@pytest.fixture
def sample_structured_outputs_choices():
return [
"Python", "Java", "JavaScript", "C++", "C#", "PHP", "TypeScript",
"Ruby", "Swift", "Kotlin"
]
@pytest.fixture
def sample_sql_statements():
return ("""
start: select_statement
select_statement: "SELECT" column "from" table "where" condition
column: "col_1" | "col_2"
table: "table_1" | "table_2"
condition: column "=" number
number: "1" | "2"
""")
@pytest.fixture(scope="session")
def zephyr_lora_files():
"""Download zephyr LoRA files once per test session."""
from huggingface_hub import snapshot_download
return snapshot_download(repo_id="typeof/zephyr-7b-beta-lora")