mirror of
https://github.com/meta-llama/llama-stack.git
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138 lines
4.3 KiB
Python
138 lines
4.3 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.providers.impls.meta_reference.evals.scorer.basic_scorers import (
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AggregateScorer,
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)
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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.dataset import * # noqa: F403
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from llama_stack.distribution.registry.datasets.dataset_registry import DatasetRegistry
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from llama_stack.providers.impls.meta_reference.evals.processor.mmlu_processor import (
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MMLUProcessor,
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)
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# from llama_stack.distribution.registry.tasks.task_registry import TaskRegistry
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# from .tasks.run_eval_task import RunEvalTask
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from .scorer.basic_scorers import * # noqa: F403
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from .config import MetaReferenceEvalsImplConfig
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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=2,
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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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]
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),
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)
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# TODO: wrap inside task
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# run_task = RunEvalTask(
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# eval_task_config=eval_task_config,
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# )
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# eval_result = run_task.run()
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dataset = DatasetRegistry.get_dataset(
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eval_task_config.dataset_config.dataset_name
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)
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dataset.load(n_samples=eval_task_config.dataset_config.row_limit)
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print(f"Running on {len(dataset)} samples")
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# F1
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processor = MMLUProcessor()
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preprocessed = processor.preprocess(dataset)
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# Generation
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# TODO: wrap inside BaseGenerator
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generation_outputs = []
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for sample in preprocessed:
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print("generation: ", sample)
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response = await self.inference_api.chat_completion(
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model=model,
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messages=sample.generation_input.messages,
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stream=False,
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)
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cprint(f"response: {response}", "cyan")
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generation_outputs.append(
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GenerationResponseSample(
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generation_output=GenerationOutput(
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completion_message=response.completion_message.content
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)
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)
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)
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cprint(generation_outputs, "green")
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# F2
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postprocessed = processor.postprocess(generation_outputs, dataset)
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cprint(postprocessed, "blue")
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# F3 - scorer
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scorer = AggregateScorer(
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scorers=[
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AccuracyScorer(),
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RandomScorer(),
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]
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)
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scorer_results = scorer.score(postprocessed)
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cprint(scorer_results, "magenta")
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eval_result = scorer.aggregate_results(scorer_results)
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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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return EvaluateResponse(
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eval_result={},
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)
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