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mmlu loose
This commit is contained in:
Xi Yan
parent
6ee02ca23b
commit
edeb6dcf04
4 changed files with 46 additions and 59 deletions
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@ -5,17 +5,17 @@
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# the root directory of this source tree.
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from llama_models.llama3.api.datatypes import URL
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from llama_stack.apis.common.type_system import CompletionInputType, StringType
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from llama_stack.apis.common.type_system import ChatCompletionInputType, StringType
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from llama_stack.apis.datasetio import DatasetDef
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llamastack_mmlu = DatasetDef(
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identifier="llamastack_mmlu",
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llamastack_mmlu_loose = DatasetDef(
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identifier="llamastack_mmlu_loose",
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url=URL(uri="https://huggingface.co/datasets/yanxi0830/ls-mmlu"),
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dataset_schema={
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"expected_answer": StringType(),
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"input_query": StringType(),
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"chat_completion_input": CompletionInputType(),
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"expected_answer": StringType(),
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"chat_completion_input": ChatCompletionInputType(),
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},
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metadata={"path": "yanxi0830/ls-mmlu", "split": "train"},
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)
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@ -13,11 +13,11 @@ from llama_stack.providers.datatypes import DatasetsProtocolPrivate
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from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
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from .config import HuggingfaceDatasetIOConfig
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from .dataset_defs.llamastack_mmlu import llamastack_mmlu
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from .dataset_defs.llamastack_mmlu_loose import llamastack_mmlu_loose
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def load_hf_dataset(dataset_def: DatasetDef):
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if dataset_def.metadata.get("dataset_path", None):
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if dataset_def.metadata.get("path", None):
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return load_dataset(**dataset_def.metadata)
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df = get_dataframe_from_url(dataset_def.url)
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@ -37,7 +37,7 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
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async def initialize(self) -> None:
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# pre-registered benchmark datasets
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self.pre_registered_datasets = [llamastack_mmlu]
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self.pre_registered_datasets = [llamastack_mmlu_loose]
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self.dataset_infos = {x.identifier: x for x in self.pre_registered_datasets}
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async def shutdown(self) -> None: ...
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@ -46,8 +46,6 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
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self,
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dataset_def: DatasetDef,
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) -> None:
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print("registering dataset", dataset_def)
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self.dataset_infos[dataset_def.identifier] = dataset_def
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async def list_datasets(self) -> List[DatasetDef]:
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@ -54,7 +54,7 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
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benchmark_tasks = [
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EvalTaskDef(
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identifier="meta-reference-mmlu",
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dataset_id="llamastack_mmlu",
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dataset_id="llamastack_mmlu_loose",
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scoring_functions=[
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"meta-reference::regex_parser_multiple_choice_answer"
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],
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@ -33,7 +33,6 @@ class Testeval:
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_, eval_tasks_impl, _, _, _, _ = eval_stack
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response = await eval_tasks_impl.list_eval_tasks()
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assert isinstance(response, list)
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assert len(response) == 0
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@pytest.mark.asyncio
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async def test_eval_evaluate_rows(self, eval_stack):
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@ -41,11 +40,6 @@ class Testeval:
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await register_dataset(
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datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval"
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)
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provider = datasetio_impl.routing_table.get_provider_impl(
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"test_dataset_for_eval"
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)
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# if provider.__provider_spec__.provider_type != "meta-reference":
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# pytest.skip("Only meta-reference provider supports registering datasets")
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response = await datasets_impl.list_datasets()
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assert len(response) >= 1
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@ -83,49 +77,44 @@ class Testeval:
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assert "meta-reference::llm_as_judge_8b_correctness" in response.scores
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assert "meta-reference::equality" in response.scores
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# @pytest.mark.asyncio
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# async def test_eval_run_eval(self, eval_stack):
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# eval_impl, eval_tasks_impl, _, _, datasetio_impl, datasets_impl = eval_stack
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# await register_dataset(
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# datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval"
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# )
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# provider = datasetio_impl.routing_table.get_provider_impl(
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# "test_dataset_for_eval"
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# )
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# if provider.__provider_spec__.provider_type != "meta-reference":
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# pytest.skip("Only meta-reference provider supports registering datasets")
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@pytest.mark.asyncio
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async def test_eval_run_eval(self, eval_stack):
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eval_impl, eval_tasks_impl, _, _, datasetio_impl, datasets_impl = eval_stack
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await register_dataset(
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datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval"
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)
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# scoring_functions = [
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# "meta-reference::llm_as_judge_8b_correctness",
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# "meta-reference::subset_of",
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# ]
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scoring_functions = [
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"meta-reference::llm_as_judge_8b_correctness",
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"meta-reference::subset_of",
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]
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# task_id = "meta-reference::app_eval-2"
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# task_def = EvalTaskDefWithProvider(
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# identifier=task_id,
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# dataset_id="test_dataset_for_eval",
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# scoring_functions=scoring_functions,
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# provider_id="meta-reference",
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# )
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# await eval_tasks_impl.register_eval_task(task_def)
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# response = await eval_impl.run_eval(
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# task_id=task_id,
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# task_config=AppEvalTaskConfig(
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# eval_candidate=ModelCandidate(
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# model="Llama3.2-3B-Instruct",
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# sampling_params=SamplingParams(),
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# ),
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# ),
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# )
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# assert response.job_id == "0"
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# job_status = await eval_impl.job_status(task_id, response.job_id)
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# assert job_status and job_status.value == "completed"
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# eval_response = await eval_impl.job_result(task_id, response.job_id)
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task_id = "meta-reference::app_eval-2"
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task_def = EvalTaskDefWithProvider(
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identifier=task_id,
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dataset_id="test_dataset_for_eval",
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scoring_functions=scoring_functions,
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provider_id="meta-reference",
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)
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await eval_tasks_impl.register_eval_task(task_def)
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response = await eval_impl.run_eval(
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task_id=task_id,
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task_config=AppEvalTaskConfig(
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eval_candidate=ModelCandidate(
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model="Llama3.2-3B-Instruct",
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sampling_params=SamplingParams(),
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),
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),
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)
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assert response.job_id == "0"
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job_status = await eval_impl.job_status(task_id, response.job_id)
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assert job_status and job_status.value == "completed"
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eval_response = await eval_impl.job_result(task_id, response.job_id)
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# assert eval_response is not None
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# assert len(eval_response.generations) == 5
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# assert "meta-reference::subset_of" in eval_response.scores
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# assert "meta-reference::llm_as_judge_8b_correctness" in eval_response.scores
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assert eval_response is not None
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assert len(eval_response.generations) == 5
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assert "meta-reference::subset_of" in eval_response.scores
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assert "meta-reference::llm_as_judge_8b_correctness" in eval_response.scores
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@pytest.mark.asyncio
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async def test_eval_run_benchmark_eval(self, eval_stack):
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@ -152,8 +141,8 @@ class Testeval:
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num_examples=3,
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),
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)
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job_status = await eval_impl.job_status(response.job_id, benchmark_id)
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job_status = await eval_impl.job_status(benchmark_id, response.job_id)
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assert job_status and job_status.value == "completed"
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eval_response = await eval_impl.job_result(response.job_id, benchmark_id)
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eval_response = await eval_impl.job_result(benchmark_id, response.job_id)
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assert eval_response is not None
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assert len(eval_response.generations) == 3
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