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https://github.com/meta-llama/llama-stack.git
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12947ac19e
# What does this PR do? This PR kills the notion of "pure passthrough" remote providers. You cannot specify a single provider you must specify a whole distribution (stack) as remote. This PR also significantly fixes / upgrades testing infrastructure so you can now test against a remotely hosted stack server by just doing ```bash pytest -s -v -m remote test_agents.py \ --inference-model=Llama3.1-8B-Instruct --safety-shield=Llama-Guard-3-1B \ --env REMOTE_STACK_URL=http://localhost:5001 ``` Also fixed `test_agents_persistence.py` (which was broken) and killed some deprecated testing functions. ## Test Plan All the tests.
132 lines
4.5 KiB
Python
132 lines
4.5 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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from pathlib import Path
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import pytest
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from PIL import Image as PIL_Image
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from .utils import group_chunks
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THIS_DIR = Path(__file__).parent
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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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ImageMedia(image=PIL_Image.open(THIS_DIR / "pasta.jpeg")),
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["spaghetti"],
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),
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(
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ImageMedia(
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image=URL(
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uri="https://www.healthypawspetinsurance.com/Images/V3/DogAndPuppyInsurance/Dog_CTA_Desktop_HeroImage.jpg"
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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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provider = inference_impl.routing_table.get_provider_impl(inference_model)
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if provider.__provider_spec__.provider_type not in (
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"meta-reference",
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"remote::together",
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"remote::fireworks",
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"remote::ollama",
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"remote::vllm",
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):
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pytest.skip(
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"Other inference providers don't support vision chat completion() yet"
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)
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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(content=[image, "Describe this image in two sentences."]),
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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(
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self, inference_model, inference_stack
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):
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inference_impl, _ = inference_stack
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provider = inference_impl.routing_table.get_provider_impl(inference_model)
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if provider.__provider_spec__.provider_type not in (
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"meta-reference",
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"remote::together",
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"remote::fireworks",
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"remote::ollama",
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"remote::vllm",
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):
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pytest.skip(
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"Other inference providers don't support vision chat completion() yet"
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)
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images = [
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ImageMedia(
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image=URL(
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uri="https://www.healthypawspetinsurance.com/Images/V3/DogAndPuppyInsurance/Dog_CTA_Desktop_HeroImage.jpg"
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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):
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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=[image, "Describe this image in two sentences."]
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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(
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isinstance(chunk, ChatCompletionResponseStreamChunk)
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for chunk in response
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)
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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(
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chunk.event.delta
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for chunk in grouped[ChatCompletionResponseEventType.progress]
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)
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for expected_string in expected_strings:
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assert expected_string in content
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