backend_demo / app.py
Clémentine
Cleaned and refactored the code, improved filtering, added selection of deleted models
8c49cb6
raw
history blame
20.1 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
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)
# 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 = 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)
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")
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 out_path.split("eval-queue/")[1].lower() 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, 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
def filter_models(
df: pd.DataFrame, current_columns_df: pd.DataFrame, type_query: str, size_query: str, 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] is True][current_columns]
if type_query != "all":
type_emoji = type_query[0]
filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name] == type_emoji]
if size_query != "all":
numeric_intervals = {
"all": (0, 10000),
"< 1B": (0, 1),
"~3B": (1, 5),
"~7B": (6, 11),
"~13B": (12, 15),
"~35B": (16, 55),
"60B+": (55, 10000),
}
numeric_interval = numeric_intervals[size_query]
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
filtered_df = filtered_df[params_column.between(*numeric_interval)]
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 models removed from the hub", 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.Radio(
label="⏚ Filter model types",
choices=[
"all",
ModelType.PT.to_str(),
ModelType.FT.to_str(),
ModelType.IFT.to_str(),
ModelType.RL.to_str(),
],
value="all",
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_size = gr.Radio(
label="⏚ Filter model sizes",
choices=[
"all",
"< 1B",
"~3B",
"~7B",
"~13B",
"~35B",
"60B+",
],
value="all",
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)",
],
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,
],
)
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()