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c0ee512980
# What does this PR do? - Remove hardcoded configurations from pre-commit. - Allow configuration to be set via pyproject.toml. - Merge .ruff.toml settings into pyproject.toml. - Ensure the linter and formatter use the defined configuration instead of being overridden by pre-commit. Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) Signed-off-by: Sébastien Han <seb@redhat.com>
119 lines
4.2 KiB
Python
119 lines
4.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import base64
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from pathlib import Path
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import pytest
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from llama_stack.apis.common.content_types import URL, ImageContentItem, TextContentItem
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from llama_stack.apis.inference import (
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ChatCompletionResponse,
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ChatCompletionResponseEventType,
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ChatCompletionResponseStreamChunk,
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SamplingParams,
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UserMessage,
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)
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from .utils import group_chunks
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THIS_DIR = Path(__file__).parent
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with open(THIS_DIR / "pasta.jpeg", "rb") as f:
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PASTA_IMAGE = base64.b64encode(f.read()).decode("utf-8")
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class TestVisionModelInference:
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"image, expected_strings",
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[
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(
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ImageContentItem(image=dict(data=PASTA_IMAGE)),
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["spaghetti"],
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),
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(
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ImageContentItem(
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image=dict(
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url=URL(
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uri="https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/client-sdk/inference/dog.png"
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)
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)
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),
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["puppy"],
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),
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],
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)
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async def test_vision_chat_completion_non_streaming(
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self, inference_model, inference_stack, image, expected_strings
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):
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inference_impl, _ = inference_stack
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response = await inference_impl.chat_completion(
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model_id=inference_model,
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messages=[
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UserMessage(content="You are a helpful assistant."),
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UserMessage(
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content=[
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image,
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TextContentItem(text="Describe this image in two sentences."),
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]
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),
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],
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stream=False,
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sampling_params=SamplingParams(max_tokens=100),
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)
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assert isinstance(response, ChatCompletionResponse)
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assert response.completion_message.role == "assistant"
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assert isinstance(response.completion_message.content, str)
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for expected_string in expected_strings:
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assert expected_string in response.completion_message.content
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@pytest.mark.asyncio
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async def test_vision_chat_completion_streaming(self, inference_model, inference_stack):
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inference_impl, _ = inference_stack
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images = [
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ImageContentItem(
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image=dict(
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url=URL(
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uri="https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/client-sdk/inference/dog.png"
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)
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)
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),
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]
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expected_strings_to_check = [
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["puppy"],
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]
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for image, expected_strings in zip(images, expected_strings_to_check, strict=False):
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response = [
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r
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async for r in await inference_impl.chat_completion(
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model_id=inference_model,
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messages=[
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UserMessage(content="You are a helpful assistant."),
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UserMessage(
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content=[
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image,
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TextContentItem(text="Describe this image in two sentences."),
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]
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),
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],
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stream=True,
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sampling_params=SamplingParams(max_tokens=100),
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)
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]
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assert len(response) > 0
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assert all(isinstance(chunk, ChatCompletionResponseStreamChunk) for chunk in response)
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grouped = group_chunks(response)
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assert len(grouped[ChatCompletionResponseEventType.start]) == 1
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assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
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assert len(grouped[ChatCompletionResponseEventType.complete]) == 1
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content = "".join(chunk.event.delta.text for chunk in grouped[ChatCompletionResponseEventType.progress])
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for expected_string in expected_strings:
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assert expected_string in content
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