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https://github.com/meta-llama/llama-stack.git
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71 lines
2.4 KiB
Python
71 lines
2.4 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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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 termcolor import cprint
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from llama_stack.distribution.registry.datasets.dataset_registry import DatasetRegistry
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from llama_stack.distribution.registry.tasks.task_registry import TaskRegistry
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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_evals(
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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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dataset = DatasetRegistry.get_dataset(dataset)
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dataset.load()
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task_impl = TaskRegistry.get_task(task)(dataset)
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x1 = task_impl.preprocess()
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# TODO: replace w/ batch inference & async return eval job
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generation_outputs = []
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if eval_task_config is None:
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eval_task_config = EvaluateTaskConfig(n_samples=len(x1))
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if eval_task_config.n_samples is None or eval_task_config.n_samples > len(x1):
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eval_task_config.n_samples = len(x1)
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print(
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f"Eval generation start, generate on {eval_task_config.n_samples} samples"
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)
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for msg in x1[: eval_task_config.n_samples]:
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print("generation for msg: ", msg)
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response = await self.inference_api.chat_completion(
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model=model,
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messages=[msg],
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stream=False,
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)
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generation_outputs.append(response.completion_message.content)
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x2 = task_impl.postprocess(generation_outputs)
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eval_results = task_impl.score(x2)
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eval_response = task_impl.aggregate_results(eval_results)
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return eval_response
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