mirror of
https://github.com/meta-llama/llama-stack.git
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83 lines
2.6 KiB
Python
83 lines
2.6 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 json
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from termcolor import cprint
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.apis.evals import * # noqa: F403
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from llama_stack.apis.datasets import * # noqa: F403
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from .config import MetaReferenceEvalsImplConfig
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from .tasks.run_eval_task import RunEvalTask
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class MetaReferenceEvalsImpl(Evals):
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def __init__(self, config: MetaReferenceEvalsImplConfig, inference_api: Inference):
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self.inference_api = inference_api
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def run_eval_task(
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self,
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model: str,
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task: str,
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dataset: Optional[str] = None,
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eval_task_config: Optional[EvaluateTaskConfig] = None,
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) -> EvaluateResponse:
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cprint(
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f"model={model}, dataset={dataset}, task={task}, eval_task_config={eval_task_config}",
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"red",
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)
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if not dataset:
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raise ValueError("dataset must be specified for mete-reference evals")
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if not eval_task_config:
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# construct eval task config from inputs
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eval_task_config = EvaluateTaskConfig(
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dataset_config=EvaluateDatasetConfig(
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dataset_name=dataset,
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row_limit=3,
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),
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processor_config=EvaluateProcessorConfig(
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processor_identifier="mmlu",
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),
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generation_config=EvaluateModelGenerationConfig(
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model=model,
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),
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scoring_config=EvaluateScoringConfig(
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scorer_config_list=[
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EvaluateSingleScorerConfig(scorer_name="accuracy"),
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EvaluateSingleScorerConfig(scorer_name="random"),
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]
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),
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)
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run_task = RunEvalTask()
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eval_result = await run_task.run(eval_task_config, self.inference_api)
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return EvaluateResponse(
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eval_result=eval_result,
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formatted_report=json.dumps(eval_result.json(), indent=4),
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)
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async def run_scorer(
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self,
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dataset_config: EvaluateDatasetConfig,
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eval_scoring_config: EvaluateScoringConfig,
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) -> EvaluateResponse:
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cprint("run_scorer")
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# main logic, we need to convert the datset into List[ScorerInputSample]
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return EvaluateResponse(
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eval_result={},
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)
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