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from lm_eval import tasks, evaluator, utils
from lm_eval.tasks import initialize_tasks, TaskManager
try:
from lm_eval.tasks import include_task_folder
except:
from lm_eval.tasks import include_path
from src.backend.manage_requests import EvalRequest
# from src.backend.tasks.xsum.task import XSum
# from src.backend.tasks.xsum.task_v2 import XSumv2
# from src.backend.tasks.cnndm.task import CNNDM
# from src.backend.tasks.cnndm.task_v2 import CNNDMv2
# from src.backend.tasks.selfcheckgpt.task import SelfCheckGpt
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.")
# try:
# include_task_folder("src/backend/tasks/")
# except:
# include_path("src/backend/tasks")
# initialize_tasks('INFO')
# https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage
# indexes all tasks from the `lm_eval/tasks` subdirectory.
# Alternatively, you can set `TaskManager(include_path="path/to/my/custom/task/configs")`
# to include a set of tasks in a separate directory.
task_manager = TaskManager(include_path="src/backend/open_medical_llm_leaderboard_tasks")
if "gpt" in eval_request.model:
model = "openai-chat-completions"
else:
model = "hf-auto"
print(f"Considered Tasks (after overriding): {task_names}")
print(f"model_args: {eval_request.get_model_args()}")
results = evaluator.simple_evaluate(model=model, # "hf-causal-experimental", # "hf-causal" how can i make this work for
model_args=eval_request.get_model_args(),
task_manager=task_manager,
tasks=task_names,
num_fewshot=num_fewshot,
batch_size=batch_size,
max_batch_size=8,
device=device,
use_cache=use_cache,
limit=limit,
# task_manager=task_manager,
# include_path="/Users/chaeeunlee/Documents/VSC_workspaces/biomed_probing_leaderboard/src/backend/tasks",
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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