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import argparse
import os
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
import scipy.stats
import torch
import lm_eval.evaluator
import lm_eval.models.utils
from lm_eval import tasks, utils
os.environ["TOKENIZERS_PARALLELISM"] = "false"
eval_logger = utils.eval_logger
def memory_stats():
eval_logger.info(
f"Memory allocated: {torch.cuda.memory_allocated() / 1024 ** 2}, reserved: {torch.cuda.memory_reserved() // 1024 ** 2}"
)
def calculate_z_value(res1: Dict, res2: Dict) -> Tuple[float, float]:
acc1, acc2 = res1["acc,none"], res2["acc,none"]
st_err1, st_err2 = res1["acc_stderr,none"], res2["acc_stderr,none"]
Z = (acc1 - acc2) / np.sqrt((st_err1**2) + (st_err2**2))
# Determining the p-value
p_value = 2 * scipy.stats.norm.sf(abs(Z)) # two-tailed test
return Z, p_value
def print_results(
data_to_print: List = None, results_dict: Dict = None, alpha: float = None
):
model1_data = data_to_print[0]
model2_data = data_to_print[1]
table_data = []
for task in model1_data.keys():
row = {
"Task": task,
"HF Accuracy": model1_data[task]["acc,none"],
"vLLM Accuracy": model2_data[task]["acc,none"],
"HF StdErr": model1_data[task]["acc_stderr,none"],
"vLLM StdErr": model2_data[task]["acc_stderr,none"],
}
table_data.append(row)
comparison_df = pd.DataFrame(table_data)
comparison_df["Z-Score"] = comparison_df["Task"].apply(
lambda task: results_dict[task]["z"]
)
comparison_df["P-Value"] = comparison_df["Task"].apply(
lambda task: results_dict[task]["p_value"]
)
comparison_df[f"p > {alpha}"] = comparison_df["P-Value"].apply(
lambda p: "✓" if p > alpha else "×"
)
return comparison_df
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--pretrained", default="EleutherAI/pythia-70m", help="name of model to compare"
)
parser.add_argument(
"--hf_args", help="huggingface model args <arg>=<value>", default=""
)
parser.add_argument("--vllm_args", help="vllm model args <arg>=<value>", default="")
parser.add_argument("--tasks", type=str, default="arc_easy,hellaswag")
parser.add_argument(
"--limit",
type=float,
default=100,
)
parser.add_argument(
"--alpha",
type=float,
default=0.05,
help="Significance level for two-tailed z-test",
)
parser.add_argument(
"--device",
type=str,
default="cuda",
)
parser.add_argument(
"--batch",
type=str,
default=8,
)
parser.add_argument(
"--verbosity",
type=str,
default="INFO",
help="Logging verbosity",
)
return parser.parse_args()
if __name__ == "__main__":
tasks.initialize_tasks()
args = parse_args()
tasks = args.tasks.split(",")
print(tasks)
hf_args, vllm_args = "," + args.hf_args, "," + args.vllm_args
results_vllm = lm_eval.evaluator.simple_evaluate(
model="vllm",
model_args=f"pretrained={args.pretrained}" + vllm_args,
tasks=tasks,
limit=args.limit,
device=args.device,
batch_size=args.batch,
)
memory_stats()
lm_eval.models.utils.clear_torch_cache()
eval_logger.info("Memory stats cleared")
memory_stats()
results_hf = lm_eval.evaluator.simple_evaluate(
model="hf",
model_args=f"pretrained={args.pretrained}" + hf_args,
tasks=tasks,
limit=args.limit,
device=args.device,
batch_size=args.batch,
)
all_res = {}
for task1, task2 in zip(
results_hf["results"].items(), results_vllm["results"].items()
):
assert task1[0] == task2[0]
z, p_value = calculate_z_value(task1[1], task2[1])
all_res[task1[0]] = {"z": z, "p_value": p_value}
df = print_results(
[results_hf["results"], results_vllm["results"]], all_res, args.alpha
)
print(df)
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