llama-stack-mirror/llama_stack/providers/tests/inference/test_vision_inference.py
Ashwin Bharambe d78027f3b5 Move runpod provider to the correct directory
Also cleanup the test code to avoid skipping tests. Let failures be
known and public.
2025-01-23 12:25:12 -08:00

129 lines
4.3 KiB
Python

# 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.
from pathlib import Path
import pytest
from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem, URL
from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseEventType,
ChatCompletionResponseStreamChunk,
SamplingParams,
UserMessage,
)
from .utils import group_chunks
THIS_DIR = Path(__file__).parent
with open(THIS_DIR / "pasta.jpeg", "rb") as f:
PASTA_IMAGE = f.read()
class TestVisionModelInference:
@pytest.mark.asyncio
@pytest.mark.parametrize(
"image, expected_strings",
[
(
ImageContentItem(image=dict(data=PASTA_IMAGE)),
["spaghetti"],
),
(
ImageContentItem(
image=dict(
url=URL(
uri="https://www.healthypawspetinsurance.com/Images/V3/DogAndPuppyInsurance/Dog_CTA_Desktop_HeroImage.jpg"
)
)
),
["puppy"],
),
],
)
async def test_vision_chat_completion_non_streaming(
self, inference_model, inference_stack, image, expected_strings
):
inference_impl, _ = inference_stack
response = await inference_impl.chat_completion(
model_id=inference_model,
messages=[
UserMessage(content="You are a helpful assistant."),
UserMessage(
content=[
image,
TextContentItem(text="Describe this image in two sentences."),
]
),
],
stream=False,
sampling_params=SamplingParams(max_tokens=100),
)
assert isinstance(response, ChatCompletionResponse)
assert response.completion_message.role == "assistant"
assert isinstance(response.completion_message.content, str)
for expected_string in expected_strings:
assert expected_string in response.completion_message.content
@pytest.mark.asyncio
async def test_vision_chat_completion_streaming(
self, inference_model, inference_stack
):
inference_impl, _ = inference_stack
images = [
ImageContentItem(
image=dict(
url=URL(
uri="https://www.healthypawspetinsurance.com/Images/V3/DogAndPuppyInsurance/Dog_CTA_Desktop_HeroImage.jpg"
)
)
),
]
expected_strings_to_check = [
["puppy"],
]
for image, expected_strings in zip(images, expected_strings_to_check):
response = [
r
async for r in await inference_impl.chat_completion(
model_id=inference_model,
messages=[
UserMessage(content="You are a helpful assistant."),
UserMessage(
content=[
image,
TextContentItem(
text="Describe this image in two sentences."
),
]
),
],
stream=True,
sampling_params=SamplingParams(max_tokens=100),
)
]
assert len(response) > 0
assert all(
isinstance(chunk, ChatCompletionResponseStreamChunk)
for chunk in response
)
grouped = group_chunks(response)
assert len(grouped[ChatCompletionResponseEventType.start]) == 1
assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
assert len(grouped[ChatCompletionResponseEventType.complete]) == 1
content = "".join(
chunk.event.delta.text
for chunk in grouped[ChatCompletionResponseEventType.progress]
)
for expected_string in expected_strings:
assert expected_string in content