lm-evaluation-harness/tests/test_requests_caching.py

# import lm_eval.base as base
import importlib
import os
import sys
from datetime import datetime
from typing import List, Tuple

import pytest
import torch

# import lm_eval.models as models
from lm_eval.caching.cache import PATH


MODULE_DIR = os.path.dirname(os.path.realpath(__file__))

# NOTE the script this loads uses simple evaluate
# TODO potentially test both the helper script and the normal script
sys.path.append(f"{MODULE_DIR}/../scripts")
model_loader = importlib.import_module("requests_caching")
run_model_for_task_caching = model_loader.run_model_for_task_caching


DEFAULT_TASKS = ["lambada_openai", "hellaswag"]


@pytest.fixture(autouse=True)
def setup_and_teardown():
    # Setup
    torch.use_deterministic_algorithms(False)
    clear_cache()
    # Yields control back to the test function
    yield
    # Cleanup here


def clear_cache():
    if os.path.exists(PATH):
        cache_files = os.listdir(PATH)
        for file in cache_files:
            file_path = f"{PATH}/{file}"
            os.unlink(file_path)


# leaving tasks here to allow for the option to select specific task files
def get_cache_files(tasks: List[str] = None) -> Tuple[List[str], List[str]]:
    cache_files = os.listdir(PATH)

    file_task_names = []

    for file in cache_files:
        file_without_prefix = file.split("-")[1]
        file_without_prefix_and_suffix = file_without_prefix.split(".")[0]
        file_task_names.append(file_without_prefix_and_suffix)

    return cache_files, file_task_names


def assert_created(tasks: List[str], file_task_names: List[str]):
    tasks.sort()
    file_task_names.sort()

    assert tasks == file_task_names


@pytest.mark.parametrize("tasks", [DEFAULT_TASKS])
def test_requests_caching_true(tasks: List[str]):
    run_model_for_task_caching(tasks=tasks, cache_requests="true")

    cache_files, file_task_names = get_cache_files()

    assert_created(tasks=tasks, file_task_names=file_task_names)


@pytest.mark.parametrize("tasks", [DEFAULT_TASKS])
def test_requests_caching_refresh(tasks: List[str]):
    run_model_for_task_caching(tasks=tasks, cache_requests="true")

    timestamp_before_test = datetime.now().timestamp()

    run_model_for_task_caching(tasks=tasks, cache_requests="refresh")

    cache_files, file_task_names = get_cache_files()

    for file in cache_files:
        modification_time = os.path.getmtime(f"{PATH}/{file}")
        assert modification_time > timestamp_before_test

    tasks.sort()
    file_task_names.sort()

    assert tasks == file_task_names


@pytest.mark.parametrize("tasks", [DEFAULT_TASKS])
def test_requests_caching_delete(tasks: List[str]):
    # populate the data first, rerun this test within this test for additional confidence
    test_requests_caching_true(tasks=tasks)

    run_model_for_task_caching(tasks=tasks, cache_requests="delete")

    cache_files, file_task_names = get_cache_files()

    assert len(cache_files) == 0


# useful for locally running tests through the debugger
if __name__ == "__main__":

    def run_tests():
        tests = [
            test_requests_caching_true,
            test_requests_caching_refresh,
            test_requests_caching_delete,
        ]

        for test_func in tests:
            clear_cache()
            test_func(tasks=DEFAULT_TASKS)

        print("Tests pass")

    run_tests()