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6be563434e
# What does this PR do? - see https://github.com/meta-llama/llama-stack/pull/689 <img width="591" alt="image" src="https://github.com/user-attachments/assets/76946a67-7373-43b5-8a03-0ad201aa543b" /> - leaving `tools/builtin.py` to avoid conflicts ## Test Plan - see https://github.com/meta-llama/llama-stack/pull/689 ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
75 lines
2.2 KiB
Python
75 lines
2.2 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 pytest
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import pytest_asyncio
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from llama_stack.apis.common.content_types import URL
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from llama_stack.apis.common.type_system import StringType
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from llama_stack.apis.datasets import DatasetInput
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from llama_stack.apis.models import ModelInput
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from llama_stack.distribution.datatypes import Api, Provider
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from llama_stack.providers.tests.resolver import construct_stack_for_test
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from ..conftest import ProviderFixture
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@pytest.fixture(scope="session")
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def post_training_torchtune() -> ProviderFixture:
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="torchtune",
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provider_type="inline::torchtune",
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config={},
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)
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],
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)
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POST_TRAINING_FIXTURES = ["torchtune"]
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@pytest_asyncio.fixture(scope="session")
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async def post_training_stack(request):
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fixture_dict = request.param
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providers = {}
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provider_data = {}
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for key in ["post_training", "datasetio"]:
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fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
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providers[key] = fixture.providers
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if fixture.provider_data:
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provider_data.update(fixture.provider_data)
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test_stack = await construct_stack_for_test(
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[Api.post_training, Api.datasetio],
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providers,
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provider_data,
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models=[ModelInput(model_id="meta-llama/Llama-3.2-3B-Instruct")],
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datasets=[
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DatasetInput(
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dataset_id="alpaca",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/tatsu-lab/alpaca"),
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metadata={
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"path": "tatsu-lab/alpaca",
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"split": "train",
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},
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dataset_schema={
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"instruction": StringType(),
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"input": StringType(),
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"output": StringType(),
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"text": StringType(),
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},
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),
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],
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)
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return test_stack.impls[Api.post_training]
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