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60 lines
1.8 KiB
Python
60 lines
1.8 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.tasks.task_registry import TaskRegistry
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from llama_stack.providers.impls.meta_reference.evals.datas.dataset_registry import (
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get_dataset,
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)
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# from llama_stack.providers.impls.meta_reference.evals.tasks.task_registry import (
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# get_task,
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# )
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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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dataset: str,
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task: str,
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) -> EvaluateResponse:
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cprint(f"model={model}, dataset={dataset}, task={task}", "red")
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dataset = get_dataset(dataset)
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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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for msg in x1[:5]:
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response = 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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async for x in response:
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generation_outputs.append(x.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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