Clémentine
Added rate limiting system to the leaderboard to prevent abuse
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import json
import os
from datetime import datetime, timezone
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi
from src.assets.css_html_js import custom_css, get_window_url_params
from src.assets.text_content import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display_models.get_model_metadata import DO_NOT_SUBMIT_MODELS, ModelType
from src.display_models.utils import (
AutoEvalColumn,
EvalQueueColumn,
fields,
styled_error,
styled_message,
styled_warning,
)
from src.load_from_hub import get_evaluation_queue_df, get_leaderboard_df, is_model_on_hub, load_all_info_from_hub
from src.rate_limiting import user_submission_permission
pd.set_option("display.precision", 1)
# clone / pull the lmeh eval data
H4_TOKEN = os.environ.get("H4_TOKEN", None)
QUEUE_REPO = "open-llm-leaderboard/requests"
RESULTS_REPO = "open-llm-leaderboard/results"
PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
EVAL_REQUESTS_PATH = "eval-queue"
EVAL_RESULTS_PATH = "eval-results"
EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
api = HfApi(token=H4_TOKEN)
def restart_space():
api.restart_space(repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN)
# Rate limit variables
RATE_LIMIT_PERIOD = 7
RATE_LIMIT_QUOTA = 5
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
if not IS_PUBLIC:
COLS.insert(2, AutoEvalColumn.precision.name)
TYPES.insert(2, AutoEvalColumn.precision.type)
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
BENCHMARK_COLS = [
c.name
for c in [
AutoEvalColumn.arc,
AutoEvalColumn.hellaswag,
AutoEvalColumn.mmlu,
AutoEvalColumn.truthfulqa,
]
]
## LOAD INFO FROM HUB
eval_queue, requested_models, eval_results, users_to_submission_dates = load_all_info_from_hub(
QUEUE_REPO, RESULTS_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH
)
if not IS_PUBLIC:
(eval_queue_private, requested_models_private, eval_results_private, _) = load_all_info_from_hub(
PRIVATE_QUEUE_REPO,
PRIVATE_RESULTS_REPO,
EVAL_REQUESTS_PATH_PRIVATE,
EVAL_RESULTS_PATH_PRIVATE,
)
else:
eval_queue_private, eval_results_private = None, None
original_df = get_leaderboard_df(eval_results, eval_results_private, COLS, BENCHMARK_COLS)
models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
to_be_dumped = f"models = {repr(models)}\n"
with open("models_backlinks.py", "w") as f:
f.write(to_be_dumped)
print(to_be_dumped)
leaderboard_df = original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
## INTERACTION FUNCTIONS
def add_new_eval(
model: str,
base_model: str,
revision: str,
precision: str,
private: bool,
weight_type: str,
model_type: str,
):
precision = precision.split(" ")[0]
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
num_models_submitted_in_period = user_submission_permission(model, users_to_submission_dates, RATE_LIMIT_PERIOD)
if num_models_submitted_in_period > RATE_LIMIT_QUOTA:
error_msg = f"Organisation or user `{model.split('/')[0]}`"
error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
error_msg += f"in the last {RATE_LIMIT_PERIOD} days.\n"
error_msg += "Please wait a couple of days before resubmitting, so that everybody can enjoy using the leaderboard 🤗"
return styled_error(error_msg)
if model_type is None or model_type == "":
return styled_error("Please select a model type.")
# check the model actually exists before adding the eval
if revision == "":
revision = "main"
if weight_type in ["Delta", "Adapter"]:
base_model_on_hub, error = is_model_on_hub(base_model, revision)
if not base_model_on_hub:
return styled_error(f'Base model "{base_model}" {error}')
if not weight_type == "Adapter":
model_on_hub, error = is_model_on_hub(model, revision)
if not model_on_hub:
return styled_error(f'Model "{model}" {error}')
print("adding new eval")
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"private": private,
"precision": precision,
"weight_type": weight_type,
"status": "PENDING",
"submitted_time": current_time,
"model_type": model_type,
}
user_name = ""
model_path = model
if "/" in model:
user_name = model.split("/")[0]
model_path = model.split("/")[1]
OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
# Check if the model has been forbidden:
if out_path.split("eval-queue/")[1] in DO_NOT_SUBMIT_MODELS:
return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")
# Check for duplicate submission
if f"{model}_{revision}_{precision}" in requested_models:
return styled_warning("This model has been already submitted.")
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.split("eval-queue/")[1],
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Add {model} to eval queue",
)
# remove the local file
os.remove(out_path)
return styled_message(
"Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
)
# Basics
def refresh() -> list[pd.DataFrame]:
leaderboard_df = get_leaderboard_df(eval_results, eval_results_private, COLS, BENCHMARK_COLS)
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
return (
leaderboard_df,
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
)
def change_tab(query_param: str):
query_param = query_param.replace("'", '"')
query_param = json.loads(query_param)
if isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "evaluation":
return gr.Tabs.update(selected=1)
else:
return gr.Tabs.update(selected=0)
# Searching and filtering
def search_table(df: pd.DataFrame, current_columns_df: pd.DataFrame, query: str) -> pd.DataFrame:
current_columns = current_columns_df.columns
if AutoEvalColumn.model_type.name in current_columns:
filtered_df = df[
(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))
| (df[AutoEvalColumn.model_type.name].str.contains(query, case=False))
]
else:
filtered_df = df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
return filtered_df[current_columns]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
# We use COLS to maintain sorting
filtered_df = df[
always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]
]
return filtered_df
NUMERIC_INTERVALS = {
"< 1.5B": (0, 1.5),
"~3B": (1.5, 5),
"~7B": (6, 11),
"~13B": (12, 15),
"~35B": (16, 55),
"60B+": (55, 10000),
}
def filter_models(
df: pd.DataFrame, current_columns_df: pd.DataFrame, type_query: list, size_query: list, show_deleted: bool
) -> pd.DataFrame:
current_columns = current_columns_df.columns
# Show all models
if show_deleted:
filtered_df = df[current_columns]
else: # Show only still on the hub models
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True][current_columns]
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
numeric_interval = [NUMERIC_INTERVALS[s] for s in size_query]
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
filtered_df = filtered_df[params_column.between(numeric_interval[0][0], numeric_interval[-1][1])]
return filtered_df
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[
c
for c in COLS
if c
not in [
AutoEvalColumn.dummy.name,
AutoEvalColumn.model.name,
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.still_on_hub.name,
]
],
value=[
c
for c in COLS_LITE
if c
not in [
AutoEvalColumn.dummy.name,
AutoEvalColumn.model.name,
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.still_on_hub.name,
]
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Row():
deleted_models_visibility = gr.Checkbox(
value=True, label="Show gated/private/deleted models", interactive=True
)
with gr.Column(min_width=320):
search_bar = gr.Textbox(
placeholder="🔍 Search for your model and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=[
ModelType.PT.to_str(),
ModelType.FT.to_str(),
ModelType.IFT.to_str(),
ModelType.RL.to_str(),
],
value=[
ModelType.PT.to_str(),
ModelType.FT.to_str(),
ModelType.IFT.to_str(),
ModelType.RL.to_str(),
],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name]
],
headers=[
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name],
datatype=TYPES,
max_rows=None,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
# 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=None,
visible=False,
)
search_bar.submit(
search_table,
[
hidden_leaderboard_table_for_search,
leaderboard_table,
search_bar,
],
leaderboard_table,
)
shown_columns.change(
select_columns,
[hidden_leaderboard_table_for_search, shown_columns],
leaderboard_table,
queue=False,
)
filter_columns_type.change(
filter_models,
[
hidden_leaderboard_table_for_search,
leaderboard_table,
filter_columns_type,
filter_columns_size,
deleted_models_visibility,
],
leaderboard_table,
queue=False,
)
filter_columns_size.change(
filter_models,
[
hidden_leaderboard_table_for_search,
leaderboard_table,
filter_columns_type,
filter_columns_size,
deleted_models_visibility,
],
leaderboard_table,
queue=False,
)
deleted_models_visibility.change(
filter_models,
[
hidden_leaderboard_table_for_search,
leaderboard_table,
filter_columns_type,
filter_columns_size,
deleted_models_visibility,
],
leaderboard_table,
queue=False,
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(
f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Accordion(
f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
open=False,
):
with gr.Row():
running_eval_table = gr.components.Dataframe(
value=running_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Accordion(
f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
open=False,
):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Row():
gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="revision", placeholder="main")
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
model_type = gr.Dropdown(
choices=[
ModelType.PT.to_str(" : "),
ModelType.FT.to_str(" : "),
ModelType.IFT.to_str(" : "),
ModelType.RL.to_str(" : "),
],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=[
"float16",
"bfloat16",
"8bit (LLM.int8)",
"4bit (QLoRA / FP4)",
"GPTQ"
],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=["Original", "Delta", "Adapter"],
label="Weights type",
multiselect=False,
value="Original",
interactive=True,
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
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,
precision,
private,
weight_type,
model_type,
],
submission_result,
)
with gr.Row():
refresh_button = gr.Button("Refresh")
refresh_button.click(
refresh,
inputs=[],
outputs=[
leaderboard_table,
finished_eval_table,
running_eval_table,
pending_eval_table,
],
api_name='refresh'
)
with gr.Row():
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)
dummy = gr.Textbox(visible=False)
demo.load(
change_tab,
dummy,
tabs,
_js=get_window_url_params,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.queue(concurrency_count=40).launch()