forked from opendatahub-io/lm-evaluation-harness
-
Notifications
You must be signed in to change notification settings - Fork 1
/
model_comparator.py
139 lines (124 loc) · 3.99 KB
/
model_comparator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
import argparse
import os
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
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]:
from scipy.stats.norm import sf
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 * 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)