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59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
from __future__ import annotations
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import functools
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import os
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import typing as t
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import pytest
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import transformers
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import openllm
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from bentoml._internal.configuration.containers import BentoMLContainer
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if t.TYPE_CHECKING:
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from pathlib import Path
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HF_INTERNAL_T5_TESTING = 'hf-internal-testing/tiny-random-t5'
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actions_xfail = functools.partial(pytest.mark.xfail,
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condition=os.getenv('GITHUB_ACTIONS') is not None,
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reason='Marking GitHub Actions to xfail due to flakiness and building environment not isolated.',
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)
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@actions_xfail
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def test_general_build_with_internal_testing():
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bento_store = BentoMLContainer.bento_store.get()
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llm = openllm.AutoLLM.for_model('flan-t5', model_id=HF_INTERNAL_T5_TESTING)
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bento = openllm.build('flan-t5', model_id=HF_INTERNAL_T5_TESTING)
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assert llm.llm_type == bento.info.labels['_type']
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assert llm.config['env']['backend_value'] == bento.info.labels['_framework']
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bento = openllm.build('flan-t5', model_id=HF_INTERNAL_T5_TESTING)
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assert len(bento_store.list(bento.tag)) == 1
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@actions_xfail
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def test_general_build_from_local(tmp_path_factory: pytest.TempPathFactory):
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local_path = tmp_path_factory.mktemp('local_t5')
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llm = openllm.AutoLLM.for_model('flan-t5', model_id=HF_INTERNAL_T5_TESTING, ensure_available=True)
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if isinstance(llm.model, transformers.Pipeline):
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llm.model.save_pretrained(str(local_path))
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else:
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llm.model.save_pretrained(str(local_path))
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llm.tokenizer.save_pretrained(str(local_path))
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assert openllm.build('flan-t5', model_id=local_path.resolve().__fspath__(), model_version='local')
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@pytest.fixture()
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def dockerfile_template(tmp_path_factory: pytest.TempPathFactory):
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file = tmp_path_factory.mktemp('dockerfiles') / 'Dockerfile.template'
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file.write_text("{% extends bento_base_template %}\n{% block SETUP_BENTO_ENTRYPOINT %}\n{{ super() }}\nRUN echo 'sanity from custom dockerfile'\n{% endblock %}")
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return file
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@pytest.mark.usefixtures('dockerfile_template')
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@actions_xfail
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def test_build_with_custom_dockerfile(dockerfile_template: Path):
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assert openllm.build('flan-t5', model_id=HF_INTERNAL_T5_TESTING, dockerfile_template=str(dockerfile_template))
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