open_llm_leaderboard

Running

App Files Files Community

open_llm_leaderboard / app.py

gsaivinay

updates

08be18b 8 months ago

raw history blame contribute delete

No virus

22.1 kB

	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="gsaivinay/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"

	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)

	print(leaderboard_df["Precision"].unique())


	## 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 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 update_table(
	hidden_df: pd.DataFrame,
	current_columns_df: pd.DataFrame,
	columns: list,
	type_query: list,
	precision_query: str,
	size_query: list,
	show_deleted: bool,
	query: str,
	):
	filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)


	def update_table(
	hidden_df: pd.DataFrame,
	current_columns_df: pd.DataFrame,
	columns: list,
	type_query: list,
	precision_query: str,
	size_query: list,
	show_deleted: bool,
	query: str,
	):
	filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
	if query != "":
	filtered_df = search_table(filtered_df, query)
	df = select_columns(filtered_df, columns)

	return df


	def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
	return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]


	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 = {
	"Unknown": pd.Interval(-1, 0, closed="right"),
	"< 1.5B": pd.Interval(0, 1.5, closed="right"),
	"~3B": pd.Interval(1.5, 5, closed="right"),
	"~7B": pd.Interval(6, 11, closed="right"),
	"~13B": pd.Interval(12, 15, closed="right"),
	"~35B": pd.Interval(16, 55, closed="right"),
	"60B+": pd.Interval(55, 10000, closed="right"),
	}


	def filter_models(
	df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
	) -> pd.DataFrame:
	# Show all models
	if show_deleted:
	filtered_df = df
	else: # Show only still on the hub models
	filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]

	type_emoji = [t[0] for t in type_query]
	filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
	filtered_df = filtered_df[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

	numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
	params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
	mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
	filtered_df = filtered_df.loc[mask]

	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():
	search_bar = gr.Textbox(
	placeholder=" 🔍 Search for your model and press ENTER...",
	show_label=False,
	elem_id="search-bar",
	)
	with gr.Row():
	search_bar = gr.Textbox(
	placeholder=" 🔍 Search for your model and press ENTER...",
	show_label=False,
	elem_id="search-bar",
	)
	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):
	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(),
	ModelType.Unknown.to_str(),
	],
	value=[
	ModelType.PT.to_str(),
	ModelType.FT.to_str(),
	ModelType.IFT.to_str(),
	ModelType.RL.to_str(),
	ModelType.Unknown.to_str(),
	],
	interactive=True,
	elem_id="filter-columns-type",
	)
	filter_columns_precision = gr.CheckboxGroup(
	label="Precision",
	choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
	value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
	interactive=True,
	elem_id="filter-columns-precision",
	)
	filter_columns_precision = gr.CheckboxGroup(
	label="Precision",
	choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
	value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
	interactive=True,
	elem_id="filter-columns-precision",
	)
	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(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	)
	shown_columns.change(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	queue=True,
	)
	filter_columns_type.change(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	queue=True,
	)
	filter_columns_precision.change(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	queue=True,
	)
	filter_columns_precision.change(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	queue=True,
	)
	filter_columns_size.change(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	queue=True,
	)
	deleted_models_visibility.change(
	update_table,
	[
	hidden_leaderboard_table_for_search,
	leaderboard_table,
	shown_columns,
	filter_columns_type,
	filter_columns_precision,
	filter_columns_precision,
	filter_columns_size,
	deleted_models_visibility,
	search_bar,
	],
	leaderboard_table,
	queue=True,
	)
	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!](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)",
	elem_classes="markdown-text",
	)

	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=1800)
	scheduler.start()
	demo.queue(concurrency_count=40).launch()