mirror of
https://github.com/bentoml/OpenLLM.git
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131 lines
7.3 KiB
Python
131 lines
7.3 KiB
Python
from __future__ import annotations
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import contextlib, os, sys, typing as t, attr, pytest, transformers, openllm
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from unittest import mock
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from openllm_core._configuration import GenerationConfig, ModelSettings, field_env_key
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from hypothesis import assume, given, strategies as st
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from ._strategies._configuration import make_llm_config, model_settings
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# XXX: @aarnphm fixes TypedDict behaviour in 3.11
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@pytest.mark.skipif(sys.version_info[:2] == (3, 11), reason="TypedDict in 3.11 behaves differently, so we need to fix this")
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def test_missing_default():
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with pytest.raises(ValueError, match="Missing required fields *"):
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make_llm_config("MissingDefaultId", {"name_type": "lowercase", "requirements": ["bentoml"]})
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with pytest.raises(ValueError, match="Missing required fields *"):
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make_llm_config("MissingModelId", {"default_id": "huggingface/t5-tiny-testing", "requirements": ["bentoml"]})
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with pytest.raises(ValueError, match="Missing required fields *"):
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make_llm_config("MissingArchitecture", {"default_id": "huggingface/t5-tiny-testing", "model_ids": ["huggingface/t5-tiny-testing"], "requirements": ["bentoml"],},)
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def test_forbidden_access():
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cl_ = make_llm_config(
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"ForbiddenAccess", {
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"default_id": "huggingface/t5-tiny-testing", "model_ids": ["huggingface/t5-tiny-testing", "bentoml/t5-tiny-testing"], "architecture": "PreTrainedModel", "requirements": ["bentoml"],
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},
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)
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assert pytest.raises(openllm.exceptions.ForbiddenAttributeError, cl_.__getattribute__, cl_(), "__config__",)
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assert pytest.raises(openllm.exceptions.ForbiddenAttributeError, cl_.__getattribute__, cl_(), "GenerationConfig",)
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assert pytest.raises(openllm.exceptions.ForbiddenAttributeError, cl_.__getattribute__, cl_(), "SamplingParams",)
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assert openllm.utils.lenient_issubclass(cl_.__openllm_generation_class__, GenerationConfig)
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@given(model_settings())
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def test_class_normal_gen(gen_settings: ModelSettings):
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assume(gen_settings["default_id"] and all(i for i in gen_settings["model_ids"]))
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cl_: type[openllm.LLMConfig] = make_llm_config("NotFullLLM", gen_settings)
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assert issubclass(cl_, openllm.LLMConfig)
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for key in gen_settings:
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assert object.__getattribute__(cl_, f"__openllm_{key}__") == gen_settings.__getitem__(key)
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@given(model_settings(), st.integers())
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def test_simple_struct_dump(gen_settings: ModelSettings, field1: int):
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cl_ = make_llm_config("IdempotentLLM", gen_settings, fields=(("field1", "float", field1),))
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assert cl_().model_dump()["field1"] == field1
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@given(model_settings(), st.integers())
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def test_config_derivation(gen_settings: ModelSettings, field1: int):
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cl_ = make_llm_config("IdempotentLLM", gen_settings, fields=(("field1", "float", field1),))
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new_cls = cl_.model_derivate("DerivedLLM", default_id="asdfasdf")
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assert new_cls.__openllm_default_id__ == "asdfasdf"
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@given(model_settings())
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def test_config_derived_follow_attrs_protocol(gen_settings: ModelSettings):
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cl_ = make_llm_config("AttrsProtocolLLM", gen_settings)
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assert attr.has(cl_)
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@given(model_settings(), st.integers(max_value=283473), st.floats(min_value=0.0, max_value=1.0), st.integers(max_value=283473), st.floats(min_value=0.0, max_value=1.0),)
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def test_complex_struct_dump(gen_settings: ModelSettings, field1: int, temperature: float, input_field1: int, input_temperature: float):
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cl_ = make_llm_config("ComplexLLM", gen_settings, fields=(("field1", "float", field1),), generation_fields=(("temperature", temperature),),)
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sent = cl_()
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assert sent.model_dump()["field1"] == field1
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assert sent.model_dump()["generation_config"]["temperature"] == temperature
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assert sent.model_dump(flatten=True)["field1"] == field1
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assert sent.model_dump(flatten=True)["temperature"] == temperature
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passed = cl_(field1=input_field1, temperature=input_temperature)
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assert passed.model_dump()["field1"] == input_field1
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assert passed.model_dump()["generation_config"]["temperature"] == input_temperature
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assert passed.model_dump(flatten=True)["field1"] == input_field1
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assert passed.model_dump(flatten=True)["temperature"] == input_temperature
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pas_nested = cl_(generation_config={"temperature": input_temperature}, field1=input_field1)
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assert pas_nested.model_dump()["field1"] == input_field1
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assert pas_nested.model_dump()["generation_config"]["temperature"] == input_temperature
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@contextlib.contextmanager
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def patch_env(**attrs: t.Any):
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with mock.patch.dict(os.environ, attrs, clear=True):
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yield
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def test_struct_envvar():
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with patch_env(**{field_env_key("env_llm", "field1"): "4", field_env_key("env_llm", "temperature", suffix="generation"): "0.2",}):
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class EnvLLM(openllm.LLMConfig):
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__config__ = {"default_id": "asdfasdf", "model_ids": ["asdf", "asdfasdfads"], "architecture": "PreTrainedModel",}
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field1: int = 2
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class GenerationConfig:
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temperature: float = 0.8
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sent = EnvLLM.model_construct_env()
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assert sent.field1 == 4
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assert sent["temperature"] == 0.2
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overwrite_default = EnvLLM()
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assert overwrite_default.field1 == 4
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assert overwrite_default["temperature"] == 0.2
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def test_struct_provided_fields():
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class EnvLLM(openllm.LLMConfig):
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__config__ = {"default_id": "asdfasdf", "model_ids": ["asdf", "asdfasdfads"], "architecture": "PreTrainedModel",}
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field1: int = 2
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class GenerationConfig:
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temperature: float = 0.8
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sent = EnvLLM.model_construct_env(field1=20, temperature=0.4)
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assert sent.field1 == 20
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assert sent.generation_config.temperature == 0.4
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def test_struct_envvar_with_overwrite_provided_env(monkeypatch: pytest.MonkeyPatch):
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with monkeypatch.context() as mk:
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mk.setenv(field_env_key("overwrite_with_env_available", "field1"), str(4.0))
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mk.setenv(field_env_key("overwrite_with_env_available", "temperature", suffix="generation"), str(0.2))
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sent = make_llm_config(
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"OverwriteWithEnvAvailable", {
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"default_id": "asdfasdf", "model_ids": ["asdf", "asdfasdfads"], "architecture": "PreTrainedModel"
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}, fields=(("field1", "float", 3.0),),
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).model_construct_env(field1=20.0, temperature=0.4)
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assert sent.generation_config.temperature == 0.4
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assert sent.field1 == 20.0
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@given(model_settings())
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@pytest.mark.parametrize(("return_dict", "typ"), [(True, dict), (False, transformers.GenerationConfig)])
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def test_conversion_to_transformers(return_dict: bool, typ: type[t.Any], gen_settings: ModelSettings):
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cl_ = make_llm_config("ConversionLLM", gen_settings)
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assert isinstance(cl_().to_generation_config(return_as_dict=return_dict), typ)
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@given(model_settings())
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def test_click_conversion(gen_settings: ModelSettings):
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# currently our conversion omit Union type.
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def cli_mock(**attrs: t.Any):
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return attrs
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cl_ = make_llm_config("ClickConversionLLM", gen_settings)
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wrapped = cl_.to_click_options(cli_mock)
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filtered = {k for k, v in cl_.__openllm_hints__.items() if t.get_origin(v) is not t.Union}
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click_options_filtered = [i for i in wrapped.__click_params__ if i.name and not i.name.startswith("fake_")]
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assert len(filtered) == len(click_options_filtered)
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@pytest.mark.parametrize("model_name", openllm.CONFIG_MAPPING.keys())
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def test_configuration_dict_protocol(model_name: str):
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config = openllm.AutoConfig.for_model(model_name)
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assert isinstance(config.items(), list)
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assert isinstance(config.keys(), list)
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assert isinstance(config.values(), list)
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assert isinstance(dict(config), dict)
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