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llama-stack/tests/unit/providers/inference/test_remote_vllm.py
Ben Browning 10b1056dea
fix: multiple tool calls in remote-vllm chat_completion (#2161) 
# What does this PR do?

This fixes an issue in how we used the tool_call_buf from streaming tool
calls in the remote-vllm provider where it would end up concatenating
parameters from multiple different tool call results instead of
aggregating the results from each tool call separately.

It also fixes an issue found while digging into that where we were
accidentally mixing the json string form of tool call parameters with
the string representation of the python form, which mean we'd end up
with single quotes in what should be double-quoted json strings.

Closes #1120

## Test Plan

The following tests are now passing 100% for the remote-vllm provider,
where some of the test_text_inference were failing before this change:

```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_text_inference.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"

VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/inference/test_vision_inference.py --vision-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"

```

All but one of the agent tests are passing (including the multi-tool
one). See the PR at https://github.com/vllm-project/vllm/pull/17917 and
a gist at
https://gist.github.com/bbrowning/4734240ce96b4264340caa9584e47c9e for
changes needed there, which will have to get made upstream in vLLM.

Agent tests:

```
VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic" LLAMA_STACK_CONFIG=remote-vllm python -m pytest -v tests/integration/agents/test_agents.py --text-model "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic"
````

---------

Signed-off-by: Ben Browning <bbrownin@redhat.com>
2025-05-15 11:23:29 -07:00

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import asyncio
import json
import logging
import threading
import time
from http.server import BaseHTTPRequestHandler, HTTPServer
from typing import Any
from unittest.mock import AsyncMock, patch
import pytest
import pytest_asyncio
from openai.types.chat.chat_completion_chunk import (
ChatCompletionChunk as OpenAIChatCompletionChunk,
)
from openai.types.chat.chat_completion_chunk import (
Choice as OpenAIChoice,
)
from openai.types.chat.chat_completion_chunk import (
ChoiceDelta as OpenAIChoiceDelta,
)
from openai.types.chat.chat_completion_chunk import (
ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall,
)
from openai.types.chat.chat_completion_chunk import (
ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction,
)
from openai.types.model import Model as OpenAIModel
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponseEventType,
CompletionMessage,
SystemMessage,
ToolChoice,
ToolConfig,
ToolResponseMessage,
UserMessage,
)
from llama_stack.apis.models import Model
from llama_stack.models.llama.datatypes import StopReason, ToolCall
from llama_stack.providers.remote.inference.vllm.config import VLLMInferenceAdapterConfig
from llama_stack.providers.remote.inference.vllm.vllm import (
VLLMInferenceAdapter,
_process_vllm_chat_completion_stream_response,
)
# These are unit test for the remote vllm provider
# implementation. This should only contain tests which are specific to
# the implementation details of those classes. More general
# (API-level) tests should be placed in tests/integration/inference/
#
# How to run this test:
#
# pytest tests/unit/providers/inference/test_remote_vllm.py \
# -v -s --tb=short --disable-warnings
class MockInferenceAdapterWithSleep:
def __init__(self, sleep_time: int, response: dict[str, Any]):
self.httpd = None
class DelayedRequestHandler(BaseHTTPRequestHandler):
# ruff: noqa: N802
def do_POST(self):
time.sleep(sleep_time)
self.send_response(code=200)
self.end_headers()
self.wfile.write(json.dumps(response).encode("utf-8"))
self.request_handler = DelayedRequestHandler
def __enter__(self):
httpd = HTTPServer(("", 0), self.request_handler)
self.httpd = httpd
host, port = httpd.server_address
httpd_thread = threading.Thread(target=httpd.serve_forever)
httpd_thread.daemon = True # stop server if this thread terminates
httpd_thread.start()
config = VLLMInferenceAdapterConfig(url=f"http://{host}:{port}")
inference_adapter = VLLMInferenceAdapter(config)
return inference_adapter
def __exit__(self, _exc_type, _exc_value, _traceback):
if self.httpd:
self.httpd.shutdown()
self.httpd.server_close()
@pytest.fixture(scope="module")
def mock_openai_models_list():
with patch("openai.resources.models.AsyncModels.list", new_callable=AsyncMock) as mock_list:
yield mock_list
@pytest_asyncio.fixture(scope="module")
async def vllm_inference_adapter():
config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345")
inference_adapter = VLLMInferenceAdapter(config)
inference_adapter.model_store = AsyncMock()
await inference_adapter.initialize()
return inference_adapter
@pytest.mark.asyncio
async def test_register_model_checks_vllm(mock_openai_models_list, vllm_inference_adapter):
async def mock_openai_models():
yield OpenAIModel(id="foo", created=1, object="model", owned_by="test")
mock_openai_models_list.return_value = mock_openai_models()
foo_model = Model(identifier="foo", provider_resource_id="foo", provider_id="vllm-inference")
await vllm_inference_adapter.register_model(foo_model)
mock_openai_models_list.assert_called()
@pytest.mark.asyncio
async def test_old_vllm_tool_choice(vllm_inference_adapter):
"""
Test that we set tool_choice to none when no tools are in use
to support older versions of vLLM
"""
mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference")
vllm_inference_adapter.model_store.get_model.return_value = mock_model
with patch.object(vllm_inference_adapter, "_nonstream_chat_completion") as mock_nonstream_completion:
# No tools but auto tool choice
await vllm_inference_adapter.chat_completion(
"mock-model",
[],
stream=False,
tools=None,
tool_config=ToolConfig(tool_choice=ToolChoice.auto),
)
mock_nonstream_completion.assert_called()
request = mock_nonstream_completion.call_args.args[0]
# Ensure tool_choice gets converted to none for older vLLM versions
assert request.tool_config.tool_choice == ToolChoice.none
@pytest.mark.asyncio
async def test_tool_call_response(vllm_inference_adapter):
"""Verify that tool call arguments from a CompletionMessage are correctly converted
into the expected JSON format."""
# Patch the call to vllm so we can inspect the arguments sent were correct
with patch.object(
vllm_inference_adapter.client.chat.completions, "create", new_callable=AsyncMock
) as mock_nonstream_completion:
messages = [
SystemMessage(content="You are a helpful assistant"),
UserMessage(content="How many?"),
CompletionMessage(
content="",
stop_reason=StopReason.end_of_turn,
tool_calls=[
ToolCall(
call_id="foo",
tool_name="knowledge_search",
arguments={"query": "How many?"},
arguments_json='{"query": "How many?"}',
)
],
),
ToolResponseMessage(call_id="foo", content="knowledge_search found 5...."),
]
await vllm_inference_adapter.chat_completion(
"mock-model",
messages,
stream=False,
tools=[],
tool_config=ToolConfig(tool_choice=ToolChoice.auto),
)
assert mock_nonstream_completion.call_args.kwargs["messages"][2]["tool_calls"] == [
{
"id": "foo",
"type": "function",
"function": {"name": "knowledge_search", "arguments": '{"query": "How many?"}'},
}
]
@pytest.mark.asyncio
async def test_tool_call_delta_empty_tool_call_buf():
"""
Test that we don't generate extra chunks when processing a
tool call response that didn't call any tools. Previously we would
emit chunks with spurious ToolCallParseStatus.succeeded or
ToolCallParseStatus.failed when processing chunks that didn't
actually make any tool calls.
"""
async def mock_stream():
delta = OpenAIChoiceDelta(content="", tool_calls=None)
choices = [OpenAIChoice(delta=delta, finish_reason="stop", index=0)]
mock_chunk = OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=choices,
)
for chunk in [mock_chunk]:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 2
assert chunks[0].event.event_type.value == "start"
assert chunks[1].event.event_type.value == "complete"
assert chunks[1].event.stop_reason == StopReason.end_of_turn
@pytest.mark.asyncio
async def test_tool_call_delta_streaming_arguments_dict():
async def mock_stream():
mock_chunk_1 = OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
OpenAIChoice(
delta=OpenAIChoiceDelta(
content="",
tool_calls=[
OpenAIChoiceDeltaToolCall(
id="tc_1",
index=1,
function=OpenAIChoiceDeltaToolCallFunction(
name="power",
arguments="",
),
)
],
),
finish_reason=None,
index=0,
)
],
)
mock_chunk_2 = OpenAIChatCompletionChunk(
id="chunk-2",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
OpenAIChoice(
delta=OpenAIChoiceDelta(
content="",
tool_calls=[
OpenAIChoiceDeltaToolCall(
id="tc_1",
index=1,
function=OpenAIChoiceDeltaToolCallFunction(
name="power",
arguments='{"number": 28, "power": 3}',
),
)
],
),
finish_reason=None,
index=0,
)
],
)
mock_chunk_3 = OpenAIChatCompletionChunk(
id="chunk-3",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
OpenAIChoice(delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0)
],
)
for chunk in [mock_chunk_1, mock_chunk_2, mock_chunk_3]:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 3
assert chunks[0].event.event_type.value == "start"
assert chunks[1].event.event_type.value == "progress"
assert chunks[1].event.delta.type == "tool_call"
assert chunks[1].event.delta.parse_status.value == "succeeded"
assert chunks[1].event.delta.tool_call.arguments_json == '{"number": 28, "power": 3}'
assert chunks[2].event.event_type.value == "complete"
@pytest.mark.asyncio
async def test_multiple_tool_calls():
async def mock_stream():
mock_chunk_1 = OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
OpenAIChoice(
delta=OpenAIChoiceDelta(
content="",
tool_calls=[
OpenAIChoiceDeltaToolCall(
id="",
index=1,
function=OpenAIChoiceDeltaToolCallFunction(
name="power",
arguments='{"number": 28, "power": 3}',
),
),
],
),
finish_reason=None,
index=0,
)
],
)
mock_chunk_2 = OpenAIChatCompletionChunk(
id="chunk-2",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
OpenAIChoice(
delta=OpenAIChoiceDelta(
content="",
tool_calls=[
OpenAIChoiceDeltaToolCall(
id="",
index=2,
function=OpenAIChoiceDeltaToolCallFunction(
name="multiple",
arguments='{"first_number": 4, "second_number": 7}',
),
),
],
),
finish_reason=None,
index=0,
)
],
)
mock_chunk_3 = OpenAIChatCompletionChunk(
id="chunk-3",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
OpenAIChoice(delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0)
],
)
for chunk in [mock_chunk_1, mock_chunk_2, mock_chunk_3]:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 4
assert chunks[0].event.event_type.value == "start"
assert chunks[1].event.event_type.value == "progress"
assert chunks[1].event.delta.type == "tool_call"
assert chunks[1].event.delta.parse_status.value == "succeeded"
assert chunks[1].event.delta.tool_call.arguments_json == '{"number": 28, "power": 3}'
assert chunks[2].event.event_type.value == "progress"
assert chunks[2].event.delta.type == "tool_call"
assert chunks[2].event.delta.parse_status.value == "succeeded"
assert chunks[2].event.delta.tool_call.arguments_json == '{"first_number": 4, "second_number": 7}'
assert chunks[3].event.event_type.value == "complete"
@pytest.mark.asyncio
async def test_process_vllm_chat_completion_stream_response_no_choices():
"""
Test that we don't error out when vLLM returns no choices for a
completion request. This can happen when there's an error thrown
in vLLM for example.
"""
async def mock_stream():
choices = []
mock_chunk = OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=choices,
)
for chunk in [mock_chunk]:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 1
assert chunks[0].event.event_type.value == "start"
def test_chat_completion_doesnt_block_event_loop(caplog):
loop = asyncio.new_event_loop()
loop.set_debug(True)
caplog.set_level(logging.WARNING)
# Log when event loop is blocked for more than 200ms
loop.slow_callback_duration = 0.5
# Sleep for 500ms in our delayed http response
sleep_time = 0.5
mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference")
mock_response = {
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1,
"modle": "mock-model",
"choices": [
{
"message": {"content": ""},
"logprobs": None,
"finish_reason": "stop",
"index": 0,
}
],
}
async def do_chat_completion():
await inference_adapter.chat_completion(
"mock-model",
[],
stream=False,
tools=None,
tool_config=ToolConfig(tool_choice=ToolChoice.auto),
)
with MockInferenceAdapterWithSleep(sleep_time, mock_response) as inference_adapter:
inference_adapter.model_store = AsyncMock()
inference_adapter.model_store.get_model.return_value = mock_model
loop.run_until_complete(inference_adapter.initialize())
# Clear the logs so far and run the actual chat completion we care about
caplog.clear()
loop.run_until_complete(do_chat_completion())
# Ensure we don't have any asyncio warnings in the captured log
# records from our chat completion call. A message gets logged
# here any time we exceed the slow_callback_duration configured
# above.
asyncio_warnings = [record.message for record in caplog.records if record.name == "asyncio"]
assert not asyncio_warnings
@pytest.mark.asyncio
async def test_get_params_empty_tools(vllm_inference_adapter):
request = ChatCompletionRequest(
tools=[],
model="test_model",
messages=[UserMessage(content="test")],
)
params = await vllm_inference_adapter._get_params(request)
assert "tools" not in params
@pytest.mark.asyncio
async def test_process_vllm_chat_completion_stream_response_tool_call_args_last_chunk():
"""
Tests the edge case where the model returns the arguments for the tool call in the same chunk that
contains the finish reason (i.e., the last one).
We want to make sure the tool call is executed in this case, and the parameters are passed correctly.
"""
mock_tool_name = "mock_tool"
mock_tool_arguments = {"arg1": 0, "arg2": 100}
mock_tool_arguments_str = json.dumps(mock_tool_arguments)
async def mock_stream():
mock_chunks = [
OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
{
"delta": {
"content": None,
"tool_calls": [
{
"index": 0,
"id": "mock_id",
"type": "function",
"function": {
"name": mock_tool_name,
"arguments": None,
},
}
],
},
"finish_reason": None,
"logprobs": None,
"index": 0,
}
],
),
OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
{
"delta": {
"content": None,
"tool_calls": [
{
"index": 0,
"id": None,
"function": {
"name": None,
"arguments": mock_tool_arguments_str,
},
}
],
},
"finish_reason": "tool_calls",
"logprobs": None,
"index": 0,
}
],
),
]
for chunk in mock_chunks:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 3
assert chunks[-1].event.event_type == ChatCompletionResponseEventType.complete
assert chunks[-2].event.delta.type == "tool_call"
assert chunks[-2].event.delta.tool_call.tool_name == mock_tool_name
assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments
@pytest.mark.asyncio
async def test_process_vllm_chat_completion_stream_response_no_finish_reason():
"""
Tests the edge case where the model requests a tool call and stays idle without explicitly providing the
finish reason.
We want to make sure that this case is recognized and handled correctly, i.e., as a valid end of message.
"""
mock_tool_name = "mock_tool"
mock_tool_arguments = {"arg1": 0, "arg2": 100}
mock_tool_arguments_str = '"{\\"arg1\\": 0, \\"arg2\\": 100}"'
async def mock_stream():
mock_chunks = [
OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
{
"delta": {
"content": None,
"tool_calls": [
{
"index": 0,
"id": "mock_id",
"type": "function",
"function": {
"name": mock_tool_name,
"arguments": mock_tool_arguments_str,
},
}
],
},
"finish_reason": None,
"logprobs": None,
"index": 0,
}
],
),
]
for chunk in mock_chunks:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 3
assert chunks[-1].event.event_type == ChatCompletionResponseEventType.complete
assert chunks[-2].event.delta.type == "tool_call"
assert chunks[-2].event.delta.tool_call.tool_name == mock_tool_name
assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments
@pytest.mark.asyncio
async def test_process_vllm_chat_completion_stream_response_tool_without_args():
"""
Tests the edge case where no arguments are provided for the tool call.
Tool calls with no arguments should be treated as regular tool calls, which was not the case until now.
"""
mock_tool_name = "mock_tool"
async def mock_stream():
mock_chunks = [
OpenAIChatCompletionChunk(
id="chunk-1",
created=1,
model="foo",
object="chat.completion.chunk",
choices=[
{
"delta": {
"content": None,
"tool_calls": [
{
"index": 0,
"id": "mock_id",
"type": "function",
"function": {
"name": mock_tool_name,
"arguments": "",
},
}
],
},
"finish_reason": None,
"logprobs": None,
"index": 0,
}
],
),
]
for chunk in mock_chunks:
yield chunk
chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
assert len(chunks) == 3
assert chunks[-1].event.event_type == ChatCompletionResponseEventType.complete
assert chunks[-2].event.delta.type == "tool_call"
assert chunks[-2].event.delta.tool_call.tool_name == mock_tool_name
assert chunks[-2].event.delta.tool_call.arguments == {}
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