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import os | |
import json | |
from datetime import datetime, timezone | |
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) ⬆️", | |
"model_name_for_query", # dummy column to implement search bar (hidden by custom CSS) | |
] | |
TYPES = ["markdown", "str", "number", "number", "number", "number", "number", "str"] | |
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_df(): | |
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, | |
"model_name_for_query": "GPT-4", | |
} | |
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, | |
"model_name_for_query": "GPT-3.5", | |
} | |
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, | |
"model_name_for_query": "baseline", | |
} | |
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_evaluation_queue_df(): | |
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] | |
original_df = get_leaderboard_df() | |
leaderboard_df = original_df.copy() | |
( | |
finished_eval_queue_df, | |
running_eval_queue_df, | |
pending_eval_queue_df, | |
) = get_evaluation_queue_df() | |
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, | |
): | |
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | |
# 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", | |
"submitted_time": current_time, | |
} | |
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_df = get_leaderboard_df() | |
( | |
finished_eval_queue_df, | |
running_eval_queue_df, | |
pending_eval_queue_df, | |
) = get_evaluation_queue_df() | |
return ( | |
leaderboard_df, | |
finished_eval_queue_df, | |
running_eval_queue_df, | |
pending_eval_queue_df, | |
) | |
def search_table(df, query): | |
filtered_df = df[df["model_name_for_query"].str.contains(query, case=False)] | |
return filtered_df | |
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); | |
} | |
#leaderboard-table { | |
margin-top: 15px | |
} | |
#search-bar-table-box > div:first-child { | |
background: none; | |
border: none; | |
} | |
#search-bar { | |
padding: 0px; | |
width: 30%; | |
} | |
/* Hides the final column */ | |
table td:last-child, | |
table th:last-child { | |
display: none; | |
} | |
/* Limit the width of the first column so that names don't expand too much */ | |
table td:first-child, | |
table th:first-child { | |
max-width: 400px; | |
overflow: auto; | |
white-space: nowrap; | |
} | |
""" | |
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") | |
with gr.Box(elem_id="search-bar-table-box"): | |
search_bar = gr.Textbox( | |
placeholder="🔍 Search your model and press ENTER...", | |
show_label=False, | |
elem_id="search-bar", | |
) | |
leaderboard_table = gr.components.Dataframe( | |
value=leaderboard_df, | |
headers=COLS, | |
datatype=TYPES, | |
max_rows=5, | |
elem_id="leaderboard-table", | |
) | |
# Dummy leaderboard for handling the case when the user uses backspace key | |
hidden_leaderboard_table_for_search = gr.components.Dataframe( | |
value=original_df, headers=COLS, datatype=TYPES, max_rows=5, visible=False | |
) | |
search_bar.submit( | |
search_table, | |
[hidden_leaderboard_table_for_search, search_bar], | |
leaderboard_table, | |
) | |
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_df, | |
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_df, | |
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_df, | |
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.queue(concurrency_count=40).launch() | |