sheonhan's picture
Add citation button
2a73469
raw history blame
No virus
12.7 kB
import os
import json
import numpy as np
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from content import *
from huggingface_hub import Repository, HfApi
from transformers import AutoConfig
from utils import get_eval_results_dicts, make_clickable_model
# clone / pull the lmeh eval data
H4_TOKEN = os.environ.get("H4_TOKEN", None)
LMEH_REPO = "HuggingFaceH4/lmeh_evaluations"
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", None))
api = HfApi()
def restart_space():
api.restart_space(repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN)
def get_all_requested_models(requested_models_dir):
depth = 1
file_names = []
for root, dirs, files in os.walk(requested_models_dir):
current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
if current_depth == depth:
file_names.extend([os.path.join(root, file) for file in files])
return set([file_name.lower().split("./evals/")[1] for file_name in file_names])
repo = None
requested_models = None
if H4_TOKEN:
print("Pulling evaluation requests and results.")
# try:
# shutil.rmtree("./evals/")
# except:
# pass
repo = Repository(
local_dir="./evals/",
clone_from=LMEH_REPO,
use_auth_token=H4_TOKEN,
repo_type="dataset",
)
repo.git_pull()
requested_models_dir = "./evals/eval_requests"
requested_models = get_all_requested_models(requested_models_dir)
# parse the results
BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"]
METRICS = ["acc_norm", "acc_norm", "acc_norm", "mc2"]
def load_results(model, benchmark, metric):
file_path = os.path.join("evals", model, f"{model}-eval_{benchmark}.json")
if not os.path.exists(file_path):
return 0.0, None
with open(file_path) as fp:
data = json.load(fp)
accs = np.array([v[metric] for k, v in data["results"].items()])
mean_acc = np.mean(accs)
return mean_acc, data["config"]["model_args"]
COLS = [
"Model",
"Revision",
"Average ⬆️",
"ARC (25-shot) ⬆️",
"HellaSwag (10-shot) ⬆️",
"MMLU (5-shot) ⬆️",
"TruthfulQA (0-shot) ⬆️",
]
TYPES = [
"markdown",
"str",
"number",
"number",
"number",
"number",
"number",
]
if not IS_PUBLIC:
COLS.insert(2, "8bit")
TYPES.insert(2, "bool")
EVAL_COLS = ["model", "revision", "private", "8bit_eval", "is_delta_weight", "status"]
EVAL_TYPES = ["markdown", "str", "bool", "bool", "bool", "str"]
BENCHMARK_COLS = [
"ARC (25-shot) ⬆️",
"HellaSwag (10-shot) ⬆️",
"MMLU (5-shot) ⬆️",
"TruthfulQA (0-shot) ⬆️",
]
def has_no_nan_values(df, columns):
return df[columns].notna().all(axis=1)
def has_nan_values(df, columns):
return df[columns].isna().any(axis=1)
def get_leaderboard():
if repo:
print("Pulling evaluation results for the leaderboard.")
repo.git_pull()
all_data = get_eval_results_dicts(IS_PUBLIC)
if not IS_PUBLIC:
gpt4_values = {
"Model": f'<a target="_blank" href=https://arxiv.org/abs/2303.08774 style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gpt4</a>',
"Revision": "tech report",
"8bit": None,
"Average ⬆️": 84.3,
"ARC (25-shot) ⬆️": 96.3,
"HellaSwag (10-shot) ⬆️": 95.3,
"MMLU (5-shot) ⬆️": 86.4,
"TruthfulQA (0-shot) ⬆️": 59.0,
}
all_data.append(gpt4_values)
gpt35_values = {
"Model": f'<a target="_blank" href=https://arxiv.org/abs/2303.08774 style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gpt3.5</a>',
"Revision": "tech report",
"8bit": None,
"Average ⬆️": 71.9,
"ARC (25-shot) ⬆️": 85.2,
"HellaSwag (10-shot) ⬆️": 85.5,
"MMLU (5-shot) ⬆️": 70.0,
"TruthfulQA (0-shot) ⬆️": 47.0,
}
all_data.append(gpt35_values)
base_line = {
"Model": "<p>Baseline</p>",
"Revision": "N/A",
"8bit": None,
"Average ⬆️": 25.0,
"ARC (25-shot) ⬆️": 25.0,
"HellaSwag (10-shot) ⬆️": 25.0,
"MMLU (5-shot) ⬆️": 25.0,
"TruthfulQA (0-shot) ⬆️": 25.0,
}
all_data.append(base_line)
df = pd.DataFrame.from_records(all_data)
df = df.sort_values(by=["Average ⬆️"], ascending=False)
df = df[COLS]
# filter out if any of the benchmarks have not been produced
df = df[has_no_nan_values(df, BENCHMARK_COLS)]
return df
def get_eval_table():
if repo:
print("Pulling changes for the evaluation queue.")
repo.git_pull()
entries = [
entry
for entry in os.listdir("evals/eval_requests")
if not entry.startswith(".")
]
all_evals = []
for entry in entries:
if ".json" in entry:
file_path = os.path.join("evals/eval_requests", entry)
with open(file_path) as fp:
data = json.load(fp)
data["# params"] = "unknown"
data["model"] = make_clickable_model(data["model"])
data["revision"] = data.get("revision", "main")
all_evals.append(data)
else:
# this is a folder
sub_entries = [
e
for e in os.listdir(f"evals/eval_requests/{entry}")
if not e.startswith(".")
]
for sub_entry in sub_entries:
file_path = os.path.join("evals/eval_requests", entry, sub_entry)
with open(file_path) as fp:
data = json.load(fp)
# data["# params"] = get_n_params(data["model"])
data["model"] = make_clickable_model(data["model"])
all_evals.append(data)
pending_list = [e for e in all_evals if e["status"] == "PENDING"]
running_list = [e for e in all_evals if e["status"] == "RUNNING"]
finished_list = [e for e in all_evals if e["status"] == "FINISHED"]
df_pending = pd.DataFrame.from_records(pending_list)
df_running = pd.DataFrame.from_records(running_list)
df_finished = pd.DataFrame.from_records(finished_list)
return df_finished[EVAL_COLS], df_running[EVAL_COLS], df_pending[EVAL_COLS]
leaderboard = get_leaderboard()
finished_eval_queue, running_eval_queue, pending_eval_queue = get_eval_table()
def is_model_on_hub(model_name, revision) -> bool:
try:
config = AutoConfig.from_pretrained(model_name, revision=revision)
return True
except Exception as e:
print("Could not get the model config from the hub.")
print(e)
return False
def add_new_eval(
model: str,
base_model: str,
revision: str,
is_8_bit_eval: bool,
private: bool,
is_delta_weight: bool,
):
# check the model actually exists before adding the eval
if revision == "":
revision = "main"
if is_delta_weight and not is_model_on_hub(base_model, revision):
error_message = f'Base model "{base_model}" was not found on hub!'
print(error_message)
return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>"
if not is_model_on_hub(model, revision):
error_message = f'Model "{model}"was not found on hub!'
return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>"
print("adding new eval")
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"private": private,
"8bit_eval": is_8_bit_eval,
"is_delta_weight": is_delta_weight,
"status": "PENDING",
}
user_name = ""
model_path = model
if "/" in model:
user_name = model.split("/")[0]
model_path = model.split("/")[1]
OUT_DIR = f"eval_requests/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{is_8_bit_eval}_{is_delta_weight}.json"
# Check for duplicate submission
if out_path.lower() in requested_models:
duplicate_request_message = "This model has been already submitted."
return f"<p style='color: orange; font-size: 20px; text-align: center;'>{duplicate_request_message}</p>"
with open(out_path, "w") as f:
f.write(json.dumps(eval_entry))
api.upload_file(
path_or_fileobj=out_path,
path_in_repo=out_path,
repo_id=LMEH_REPO,
token=H4_TOKEN,
repo_type="dataset",
)
success_message = "Your request has been submitted to the evaluation queue!"
return f"<p style='color: green; font-size: 20px; text-align: center;'>{success_message}</p>"
def refresh():
leaderboard = get_leaderboard()
finished_eval_queue, running_eval_queue, pending_eval_queue = get_eval_table()
return leaderboard, finished_eval_queue, running_eval_queue, pending_eval_queue
custom_css = """
#changelog-text {
font-size: 16px !important;
}
#changelog-text h2 {
font-size: 18px !important;
}
.markdown-text {
font-size: 16px !important;
}
#citation-button span {
font-size: 16px !important;
}
#citation-button textarea {
font-size: 16px !important;
}
#citation-button > label > button {
margin: 6px;
transform: scale(1.3);
}
"""
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Row():
with gr.Column():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
).style(show_copy_button=True)
with gr.Column():
with gr.Accordion("✨ CHANGELOG", open=False):
changelog = gr.Markdown(CHANGELOG_TEXT, elem_id="changelog-text")
leaderboard_table = gr.components.Dataframe(
value=leaderboard, headers=COLS, datatype=TYPES, max_rows=5
)
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Accordion("✅ Finished Evaluations", open=False):
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Accordion("🔄 Running Evaluation Queue", open=False):
running_eval_table = gr.components.Dataframe(
value=running_eval_queue,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Accordion("⏳ Pending Evaluation Queue", open=False):
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
refresh_button = gr.Button("Refresh")
refresh_button.click(
refresh,
inputs=[],
outputs=[
leaderboard_table,
finished_eval_table,
running_eval_table,
pending_eval_table,
],
)
with gr.Accordion("Submit a new model for evaluation"):
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="revision", placeholder="main")
with gr.Column():
is_8bit_toggle = gr.Checkbox(
False, label="8 bit eval", visible=not IS_PUBLIC
)
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
is_delta_weight = gr.Checkbox(False, label="Delta weights")
base_model_name_textbox = gr.Textbox(label="base model (for delta)")
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
is_8bit_toggle,
private,
is_delta_weight,
],
submission_result,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.launch()