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from lm_eval import tasks, evaluator, utils
from lm_eval.tasks import initialize_tasks, include_task_folder
from src.backend.manage_requests import EvalRequest
import logging
logging.getLogger("openai").setLevel(logging.WARNING)
def run_evaluation(eval_request: EvalRequest, task_names, num_fewshot, batch_size, device, use_cache=None, limit=None, max_nb_samples=100) -> dict:
if limit:
print("WARNING: --limit SHOULD ONLY BE USED FOR TESTING. REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.")
include_task_folder("src/backend/tasks/")
initialize_tasks('INFO')
task_names = utils.pattern_match(task_names, tasks.ALL_TASKS)
print(f"Selected Tasks: {task_names}")
results = evaluator.simple_evaluate(model="hf-auto", # "hf-causal-experimental", # "hf-causal"
model_args=eval_request.get_model_args(),
tasks=task_names, num_fewshot=num_fewshot,
batch_size=batch_size, device=device, use_cache=use_cache,
limit=limit, write_out=True)
results["config"]["model_dtype"] = eval_request.precision
results["config"]["model_name"] = eval_request.model
results["config"]["model_sha"] = eval_request.revision
if max_nb_samples is not None:
if 'samples' in results:
samples = results['samples']
for task_name in samples.keys():
if len(samples[task_name]) > max_nb_samples:
results['samples'][task_name] = results['samples'][task_name][:max_nb_samples]
print(evaluator.make_table(results))
return results
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