881 lines
27 KiB
Python
881 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import json
|
|
import subprocess
|
|
import tempfile
|
|
from unittest.mock import AsyncMock, MagicMock, patch
|
|
|
|
import pytest
|
|
|
|
from vllm.assets.audio import AudioAsset
|
|
from vllm.entrypoints.openai.run_batch import (
|
|
BatchRequestOutput,
|
|
download_bytes_from_url,
|
|
)
|
|
|
|
CHAT_MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
|
|
EMBEDDING_MODEL_NAME = "intfloat/multilingual-e5-small"
|
|
RERANKER_MODEL_NAME = "BAAI/bge-reranker-v2-m3"
|
|
REASONING_MODEL_NAME = "Qwen/Qwen3-0.6B"
|
|
SPEECH_LARGE_MODEL_NAME = "openai/whisper-large-v3"
|
|
SPEECH_SMALL_MODEL_NAME = "openai/whisper-small"
|
|
|
|
INPUT_BATCH = "\n".join(
|
|
json.dumps(req)
|
|
for req in [
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": CHAT_MODEL_NAME,
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "You are a helpful assistant.",
|
|
},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": CHAT_MODEL_NAME,
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "You are an unhelpful assistant.",
|
|
},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-3",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": "NonExistModel",
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "You are an unhelpful assistant.",
|
|
},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-4",
|
|
"method": "POST",
|
|
"url": "/bad_url",
|
|
"body": {
|
|
"model": CHAT_MODEL_NAME,
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "You are an unhelpful assistant.",
|
|
},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-5",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"stream": "True",
|
|
"model": CHAT_MODEL_NAME,
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "You are an unhelpful assistant.",
|
|
},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
INVALID_INPUT_BATCH = "\n".join(
|
|
json.dumps(req)
|
|
for req in [
|
|
{
|
|
"invalid_field": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": CHAT_MODEL_NAME,
|
|
"messages": [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": CHAT_MODEL_NAME,
|
|
"messages": [
|
|
{"role": "system", "content": "You are an unhelpful assistant."},
|
|
{"role": "user", "content": "Hello world!"},
|
|
],
|
|
"max_tokens": 1000,
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
INPUT_EMBEDDING_BATCH = "\n".join(
|
|
json.dumps(req)
|
|
for req in [
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/embeddings",
|
|
"body": {
|
|
"model": EMBEDDING_MODEL_NAME,
|
|
"input": "You are a helpful assistant.",
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v1/embeddings",
|
|
"body": {
|
|
"model": EMBEDDING_MODEL_NAME,
|
|
"input": "You are an unhelpful assistant.",
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-3",
|
|
"method": "POST",
|
|
"url": "/v1/embeddings",
|
|
"body": {
|
|
"model": EMBEDDING_MODEL_NAME,
|
|
"input": "Hello world!",
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-4",
|
|
"method": "POST",
|
|
"url": "/v1/embeddings",
|
|
"body": {
|
|
"model": "NonExistModel",
|
|
"input": "Hello world!",
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
_SCORE_RERANK_DOCUMENTS = [
|
|
"The capital of Brazil is Brasilia.",
|
|
"The capital of France is Paris.",
|
|
]
|
|
|
|
INPUT_SCORE_BATCH = "\n".join(
|
|
json.dumps(req)
|
|
for req in [
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/score",
|
|
"body": {
|
|
"model": RERANKER_MODEL_NAME,
|
|
"queries": "What is the capital of France?",
|
|
"documents": _SCORE_RERANK_DOCUMENTS,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v1/score",
|
|
"body": {
|
|
"model": RERANKER_MODEL_NAME,
|
|
"queries": "What is the capital of France?",
|
|
"documents": _SCORE_RERANK_DOCUMENTS,
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
INPUT_RERANK_BATCH = "\n".join(
|
|
json.dumps(req)
|
|
for req in [
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/rerank",
|
|
"body": {
|
|
"model": RERANKER_MODEL_NAME,
|
|
"query": "What is the capital of France?",
|
|
"documents": _SCORE_RERANK_DOCUMENTS,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v1/rerank",
|
|
"body": {
|
|
"model": RERANKER_MODEL_NAME,
|
|
"query": "What is the capital of France?",
|
|
"documents": _SCORE_RERANK_DOCUMENTS,
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v2/rerank",
|
|
"body": {
|
|
"model": RERANKER_MODEL_NAME,
|
|
"query": "What is the capital of France?",
|
|
"documents": _SCORE_RERANK_DOCUMENTS,
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
INPUT_REASONING_BATCH = "\n".join(
|
|
json.dumps(req)
|
|
for req in [
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": REASONING_MODEL_NAME,
|
|
"messages": [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "Solve this math problem: 2+2=?"},
|
|
],
|
|
},
|
|
},
|
|
{
|
|
"custom_id": "request-2",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": REASONING_MODEL_NAME,
|
|
"messages": [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "What is the capital of France?"},
|
|
],
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
MINIMAL_WAV_BASE64 = "UklGRigAAABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQQAAAAAAP9/"
|
|
INPUT_TRANSCRIPTION_BATCH = (
|
|
json.dumps(
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/audio/transcriptions",
|
|
"body": {
|
|
"model": SPEECH_LARGE_MODEL_NAME,
|
|
"file_url": f"data:audio/wav;base64,{MINIMAL_WAV_BASE64}",
|
|
"response_format": "json",
|
|
},
|
|
}
|
|
)
|
|
+ "\n"
|
|
)
|
|
|
|
INPUT_TRANSCRIPTION_HTTP_BATCH = (
|
|
json.dumps(
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/audio/transcriptions",
|
|
"body": {
|
|
"model": SPEECH_LARGE_MODEL_NAME,
|
|
"file_url": AudioAsset("mary_had_lamb").url,
|
|
"response_format": "json",
|
|
},
|
|
}
|
|
)
|
|
+ "\n"
|
|
)
|
|
|
|
INPUT_TRANSLATION_BATCH = (
|
|
json.dumps(
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/audio/translations",
|
|
"body": {
|
|
"model": SPEECH_SMALL_MODEL_NAME,
|
|
"file_url": AudioAsset("mary_had_lamb").url,
|
|
"response_format": "text",
|
|
"language": "it",
|
|
"to_language": "en",
|
|
"temperature": 0.0,
|
|
},
|
|
}
|
|
)
|
|
+ "\n"
|
|
)
|
|
|
|
WEATHER_TOOL = {
|
|
"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"],
|
|
},
|
|
},
|
|
}
|
|
|
|
INPUT_TOOL_CALLING_BATCH = json.dumps(
|
|
{
|
|
"custom_id": "request-1",
|
|
"method": "POST",
|
|
"url": "/v1/chat/completions",
|
|
"body": {
|
|
"model": REASONING_MODEL_NAME,
|
|
"messages": [
|
|
{"role": "user", "content": "What is the weather in San Francisco?"},
|
|
],
|
|
"tools": [WEATHER_TOOL],
|
|
"tool_choice": "required",
|
|
"max_tokens": 1000,
|
|
},
|
|
}
|
|
)
|
|
|
|
|
|
def test_empty_file():
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write("")
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
EMBEDDING_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
assert contents.strip() == ""
|
|
|
|
|
|
def test_completions():
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
CHAT_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
# Ensure that the output format conforms to the openai api.
|
|
# Validation should throw if the schema is wrong.
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
|
|
def test_completions_invalid_input():
|
|
"""
|
|
Ensure that we fail when the input doesn't conform to the openai api.
|
|
"""
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INVALID_INPUT_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
CHAT_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode != 0, f"{proc=}"
|
|
|
|
|
|
def test_embeddings():
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_EMBEDDING_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
EMBEDDING_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
# Ensure that the output format conforms to the openai api.
|
|
# Validation should throw if the schema is wrong.
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
|
|
@pytest.mark.parametrize("input_batch", [INPUT_SCORE_BATCH, INPUT_RERANK_BATCH])
|
|
def test_score(input_batch):
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(input_batch)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
RERANKER_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
# Ensure that the output format conforms to the openai api.
|
|
# Validation should throw if the schema is wrong.
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
# Ensure that there is no error in the response.
|
|
line_dict = json.loads(line)
|
|
assert isinstance(line_dict, dict)
|
|
assert line_dict["error"] is None
|
|
|
|
|
|
def test_reasoning_parser():
|
|
"""
|
|
Test that reasoning_parser parameter works correctly in run_batch.
|
|
"""
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_REASONING_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
REASONING_MODEL_NAME,
|
|
"--reasoning-parser",
|
|
"qwen3",
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
# Ensure that the output format conforms to the openai api.
|
|
# Validation should throw if the schema is wrong.
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
# Ensure that there is no error in the response.
|
|
line_dict = json.loads(line)
|
|
assert isinstance(line_dict, dict)
|
|
assert line_dict["error"] is None
|
|
|
|
# Check that reasoning is present and not empty
|
|
reasoning = line_dict["response"]["body"]["choices"][0]["message"][
|
|
"reasoning"
|
|
]
|
|
assert reasoning is not None
|
|
assert len(reasoning) > 0
|
|
|
|
|
|
def test_transcription():
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_TRANSCRIPTION_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
SPEECH_LARGE_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
print(f"\n\ncontents: {contents}\n\n")
|
|
for line in contents.strip().split("\n"):
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
line_dict = json.loads(line)
|
|
assert isinstance(line_dict, dict)
|
|
assert line_dict["error"] is None
|
|
|
|
response_body = line_dict["response"]["body"]
|
|
assert response_body is not None
|
|
assert "text" in response_body
|
|
assert "usage" in response_body
|
|
|
|
|
|
def test_transcription_http_url():
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_TRANSCRIPTION_HTTP_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
SPEECH_LARGE_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
line_dict = json.loads(line)
|
|
assert isinstance(line_dict, dict)
|
|
assert line_dict["error"] is None
|
|
|
|
response_body = line_dict["response"]["body"]
|
|
assert response_body is not None
|
|
assert "text" in response_body
|
|
assert "usage" in response_body
|
|
|
|
transcription_text = response_body["text"]
|
|
assert "Mary had a little lamb" in transcription_text
|
|
|
|
|
|
def test_translation():
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_TRANSLATION_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
SPEECH_SMALL_MODEL_NAME,
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
line_dict = json.loads(line)
|
|
assert isinstance(line_dict, dict)
|
|
assert line_dict["error"] is None
|
|
|
|
response_body = line_dict["response"]["body"]
|
|
assert response_body is not None
|
|
assert "text" in response_body
|
|
|
|
translation_text = response_body["text"]
|
|
translation_text_lower = str(translation_text).strip().lower()
|
|
assert "mary" in translation_text_lower or "lamb" in translation_text_lower
|
|
|
|
|
|
def test_tool_calling():
|
|
"""
|
|
Test that tool calling works correctly in run_batch.
|
|
Verifies that requests with tools return tool_calls in the response.
|
|
"""
|
|
with (
|
|
tempfile.NamedTemporaryFile("w") as input_file,
|
|
tempfile.NamedTemporaryFile("r") as output_file,
|
|
):
|
|
input_file.write(INPUT_TOOL_CALLING_BATCH)
|
|
input_file.flush()
|
|
proc = subprocess.Popen(
|
|
[
|
|
"vllm",
|
|
"run-batch",
|
|
"-i",
|
|
input_file.name,
|
|
"-o",
|
|
output_file.name,
|
|
"--model",
|
|
REASONING_MODEL_NAME,
|
|
"--enable-auto-tool-choice",
|
|
"--tool-call-parser",
|
|
"hermes",
|
|
],
|
|
)
|
|
proc.communicate()
|
|
proc.wait()
|
|
assert proc.returncode == 0, f"{proc=}"
|
|
|
|
contents = output_file.read()
|
|
for line in contents.strip().split("\n"):
|
|
if not line.strip(): # Skip empty lines
|
|
continue
|
|
# Ensure that the output format conforms to the openai api.
|
|
# Validation should throw if the schema is wrong.
|
|
BatchRequestOutput.model_validate_json(line)
|
|
|
|
# Ensure that there is no error in the response.
|
|
line_dict = json.loads(line)
|
|
assert isinstance(line_dict, dict)
|
|
assert line_dict["error"] is None
|
|
|
|
# Check that tool_calls are present in the response
|
|
# With tool_choice="required", the model must call a tool
|
|
response_body = line_dict["response"]["body"]
|
|
assert response_body is not None
|
|
message = response_body["choices"][0]["message"]
|
|
assert "tool_calls" in message
|
|
tool_calls = message.get("tool_calls")
|
|
# With tool_choice="required", tool_calls must be present and non-empty
|
|
assert tool_calls is not None
|
|
assert isinstance(tool_calls, list)
|
|
assert len(tool_calls) > 0
|
|
# Verify tool_calls have the expected structure
|
|
for tool_call in tool_calls:
|
|
assert "id" in tool_call
|
|
assert "type" in tool_call
|
|
assert tool_call["type"] == "function"
|
|
assert "function" in tool_call
|
|
assert "name" in tool_call["function"]
|
|
assert "arguments" in tool_call["function"]
|
|
# Verify the tool name matches our tool definition
|
|
assert tool_call["function"]["name"] == "get_current_weather"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Unit tests for download_bytes_from_url SSRF protection
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def _make_aiohttp_mocks(response_data: bytes = b"fake-data", status: int = 200):
|
|
"""Create mock objects that simulate aiohttp.ClientSession context managers."""
|
|
mock_resp = MagicMock()
|
|
mock_resp.status = status
|
|
mock_resp.read = AsyncMock(return_value=response_data)
|
|
mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
|
|
mock_resp.__aexit__ = AsyncMock(return_value=False)
|
|
|
|
mock_session = MagicMock()
|
|
mock_session.get = MagicMock(return_value=mock_resp)
|
|
mock_session.__aenter__ = AsyncMock(return_value=mock_session)
|
|
mock_session.__aexit__ = AsyncMock(return_value=False)
|
|
return mock_session
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_data_url_bypasses_domain_check():
|
|
"""data: URLs must work regardless of the domain allowlist."""
|
|
data_url = f"data:audio/wav;base64,{MINIMAL_WAV_BASE64}"
|
|
result = await download_bytes_from_url(
|
|
data_url, allowed_media_domains=["example.com"]
|
|
)
|
|
assert isinstance(result, bytes)
|
|
assert len(result) > 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_rejects_disallowed_domain():
|
|
"""HTTP URLs whose hostname is not in the allowlist must be rejected."""
|
|
url = "https://evil.internal/secret"
|
|
with pytest.raises(ValueError, match="allowed domains"):
|
|
await download_bytes_from_url(url, allowed_media_domains=["example.com"])
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_rejects_cloud_metadata_ip():
|
|
"""Cloud metadata endpoints must be blocked when an allowlist is set."""
|
|
url = "http://169.254.169.254/latest/meta-data/"
|
|
with pytest.raises(ValueError, match="allowed domains"):
|
|
await download_bytes_from_url(url, allowed_media_domains=["example.com"])
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_rejects_internal_ip():
|
|
"""Private-range IPs must be blocked when an allowlist is set."""
|
|
for internal_url in [
|
|
"http://10.0.0.1/secret",
|
|
"http://192.168.1.1/admin",
|
|
"http://127.0.0.1:8080/internal",
|
|
]:
|
|
with pytest.raises(ValueError, match="allowed domains"):
|
|
await download_bytes_from_url(
|
|
internal_url, allowed_media_domains=["example.com"]
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_allows_permitted_domain():
|
|
"""HTTP URLs whose hostname IS in the allowlist must be fetched."""
|
|
url = "https://example.com/audio.wav"
|
|
expected = b"audio-bytes"
|
|
mock_session = _make_aiohttp_mocks(expected)
|
|
|
|
with patch(
|
|
"vllm.entrypoints.openai.run_batch.aiohttp.ClientSession",
|
|
return_value=mock_session,
|
|
):
|
|
result = await download_bytes_from_url(
|
|
url, allowed_media_domains=["example.com"]
|
|
)
|
|
assert result == expected
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_no_allowlist_permits_any_domain():
|
|
"""Without an allowlist all HTTP URLs must be attempted (backward compat)."""
|
|
url = "https://any-domain.example.org/file.wav"
|
|
expected = b"some-data"
|
|
mock_session = _make_aiohttp_mocks(expected)
|
|
|
|
with patch(
|
|
"vllm.entrypoints.openai.run_batch.aiohttp.ClientSession",
|
|
return_value=mock_session,
|
|
):
|
|
result = await download_bytes_from_url(url, allowed_media_domains=None)
|
|
assert result == expected
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_empty_allowlist_denies_all():
|
|
"""An empty allowlist must deny all HTTP URLs (least privilege)."""
|
|
url = "https://any-domain.example.org/file.wav"
|
|
with pytest.raises(ValueError, match="allowed domains"):
|
|
await download_bytes_from_url(url, allowed_media_domains=[])
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_unsupported_scheme():
|
|
"""Unsupported URL schemes must be rejected regardless of allowlist."""
|
|
with pytest.raises(ValueError, match="Unsupported URL scheme"):
|
|
await download_bytes_from_url("ftp://example.com/file.wav")
|
|
|
|
with pytest.raises(ValueError, match="Unsupported URL scheme"):
|
|
await download_bytes_from_url(
|
|
"ftp://example.com/file.wav",
|
|
allowed_media_domains=["example.com"],
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_download_bytes_backslash_bypass():
|
|
"""Backslash-@ URL confusion must not bypass the allowlist.
|
|
|
|
urllib3.parse_url() and aiohttp/yarl disagree on backslash-before-@.
|
|
The fix normalizes through urllib3 before handing to aiohttp.
|
|
"""
|
|
bypass_url = "http://allowed.example.com\\@evil.internal/secret"
|
|
with pytest.raises(ValueError, match="allowed domains"):
|
|
await download_bytes_from_url(
|
|
bypass_url, allowed_media_domains=["evil.internal"]
|
|
)
|