Spaces:
Running
Running
import os | |
import time | |
import logging | |
import gradio as gr | |
import pandas as pd | |
from apscheduler.schedulers.background import BackgroundScheduler | |
from huggingface_hub import snapshot_download | |
from gradio_space_ci import enable_space_ci | |
from src.display.about import ( | |
CITATION_BUTTON_LABEL, | |
CITATION_BUTTON_TEXT, | |
EVALUATION_QUEUE_TEXT, | |
FAQ_TEXT, | |
INTRODUCTION_TEXT, | |
LLM_BENCHMARKS_TEXT, | |
TITLE, | |
) | |
from src.display.css_html_js import custom_css | |
from src.display.utils import ( | |
BENCHMARK_COLS, | |
COLS, | |
EVAL_COLS, | |
EVAL_TYPES, | |
NUMERIC_INTERVALS, | |
TYPES, | |
AutoEvalColumn, | |
ModelType, | |
Precision, | |
WeightType, | |
fields, | |
) | |
from src.envs import ( | |
API, | |
DYNAMIC_INFO_FILE_PATH, | |
DYNAMIC_INFO_PATH, | |
DYNAMIC_INFO_REPO, | |
EVAL_REQUESTS_PATH, | |
EVAL_RESULTS_PATH, | |
H4_TOKEN, | |
IS_PUBLIC, | |
QUEUE_REPO, | |
REPO_ID, | |
RESULTS_REPO, | |
) | |
from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
from src.scripts.update_all_request_files import update_dynamic_files | |
from src.submission.submit import add_new_eval | |
from src.tools.collections import update_collections | |
from src.tools.plots import create_metric_plot_obj, create_plot_df, create_scores_df | |
# Configure logging | |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
# Start ephemeral Spaces on PRs (see config in README.md) | |
enable_space_ci() | |
def restart_space(): | |
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) | |
def time_diff_wrapper(func): | |
def wrapper(*args, **kwargs): | |
start_time = time.time() | |
result = func(*args, **kwargs) | |
end_time = time.time() | |
diff = end_time - start_time | |
logging.info(f"Time taken for {func.__name__}: {diff} seconds") | |
return result | |
return wrapper | |
def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5): | |
"""Download dataset with exponential backoff retries.""" | |
attempt = 0 | |
while attempt < max_attempts: | |
try: | |
logging.info(f"Downloading {repo_id} to {local_dir}") | |
snapshot_download( | |
repo_id=repo_id, | |
local_dir=local_dir, | |
repo_type=repo_type, | |
tqdm_class=None, | |
etag_timeout=30, | |
max_workers=8, | |
) | |
logging.info("Download successful") | |
return | |
except Exception as e: | |
wait_time = backoff_factor ** attempt | |
logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s") | |
time.sleep(wait_time) | |
attempt += 1 | |
raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts") | |
def init_space(full_init: bool = True): | |
"""Initializes the application space, loading only necessary data.""" | |
if full_init: | |
# These downloads only occur on full initialization | |
try: | |
download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH) | |
download_dataset(DYNAMIC_INFO_REPO, DYNAMIC_INFO_PATH) | |
download_dataset(RESULTS_REPO, EVAL_RESULTS_PATH) | |
except Exception: | |
restart_space() | |
# Always retrieve the leaderboard DataFrame | |
raw_data, original_df = get_leaderboard_df( | |
results_path=EVAL_RESULTS_PATH, | |
requests_path=EVAL_REQUESTS_PATH, | |
dynamic_path=DYNAMIC_INFO_FILE_PATH, | |
cols=COLS, | |
benchmark_cols=BENCHMARK_COLS, | |
) | |
if full_init: | |
# Collection update only happens on full initialization | |
update_collections(original_df) | |
leaderboard_df = original_df.copy() | |
# Evaluation queue DataFrame retrieval is independent of initialization detail level | |
eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
return leaderboard_df, raw_data, original_df, eval_queue_dfs | |
# Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set. | |
# This controls whether a full initialization should be performed. | |
do_full_init = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True" | |
# Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable. | |
# This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag. | |
leaderboard_df, raw_data, original_df, eval_queue_dfs = init_space(full_init=do_full_init) | |
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs | |
# Data processing for plots now only on demand in the respective Gradio tab | |
def load_and_create_plots(): | |
plot_df = create_plot_df(create_scores_df(raw_data)) | |
return plot_df | |
# Searching and filtering | |
def update_table( | |
hidden_df: pd.DataFrame, | |
columns: list, | |
type_query: list, | |
precision_query: str, | |
size_query: list, | |
hide_models: list, | |
query: str, | |
): | |
filtered_df = filter_models( | |
df=hidden_df, | |
type_query=type_query, | |
size_query=size_query, | |
precision_query=precision_query, | |
hide_models=hide_models, | |
) | |
filtered_df = filter_queries(query, filtered_df) | |
df = select_columns(filtered_df, columns) | |
return df | |
def load_query(request: gr.Request): # triggered only once at startup => read query parameter if it exists | |
query = request.query_params.get("query") or "" | |
return ( | |
query, | |
query, | |
) # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed | |
def search_model(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
return df[(df[AutoEvalColumn.fullname.name].str.contains(query, case=False, na=False))] | |
def search_license(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
return df[df[AutoEvalColumn.license.name].str.contains(query, case=False, na=False)] | |
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: | |
always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
dummy_col = [AutoEvalColumn.fullname.name] | |
filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col] | |
return filtered_df | |
def filter_queries(query: str, df: pd.DataFrame): | |
tmp_result_df = [] | |
# Empty query return the same df | |
if query == "": | |
return df | |
# all_queries = [q.strip() for q in query.split(";")] | |
# license_queries = [] | |
all_queries = [q.strip() for q in query.split(";") if q.strip() != ""] | |
model_queries = [q for q in all_queries if not q.startswith("licence")] | |
license_queries_raw = [q for q in all_queries if q.startswith("license")] | |
license_queries = [ | |
q.replace("license:", "").strip() for q in license_queries_raw if q.replace("license:", "").strip() != "" | |
] | |
# Handling model name search | |
for query in model_queries: | |
tmp_df = search_model(df, query) | |
if len(tmp_df) > 0: | |
tmp_result_df.append(tmp_df) | |
if not tmp_result_df and not license_queries: | |
# Nothing is found, no license_queries -> return empty df | |
return pd.DataFrame(columns=df.columns) | |
if tmp_result_df: | |
df = pd.concat(tmp_result_df) | |
df = df.drop_duplicates( | |
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] | |
) | |
if not license_queries: | |
return df | |
# Handling license search | |
tmp_result_df = [] | |
for query in license_queries: | |
tmp_df = search_license(df, query) | |
if len(tmp_df) > 0: | |
tmp_result_df.append(tmp_df) | |
if not tmp_result_df: | |
# Nothing is found, return empty df | |
return pd.DataFrame(columns=df.columns) | |
df = pd.concat(tmp_result_df) | |
df = df.drop_duplicates( | |
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] | |
) | |
return df | |
def filter_models( | |
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, hide_models: list | |
) -> pd.DataFrame: | |
# Show all models | |
if "Private or deleted" in hide_models: | |
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] | |
else: | |
filtered_df = df | |
if "Contains a merge/moerge" in hide_models: | |
filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False] | |
if "MoE" in hide_models: | |
filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False] | |
if "Flagged" in hide_models: | |
filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False] | |
type_emoji = [t[0] for t in type_query] | |
filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] | |
filtered_df = filtered_df.loc[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 | |
leaderboard_df = filter_models( | |
df=leaderboard_df, | |
type_query=[t.to_str(" : ") for t in ModelType], | |
size_query=list(NUMERIC_INTERVALS.keys()), | |
precision_query=[i.value.name for i in Precision], | |
hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs | |
) | |
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("π LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Row(): | |
search_bar = gr.Textbox( | |
placeholder="π Search models or licenses (e.g., 'model_name; license: MIT') and press ENTER...", | |
show_label=False, | |
elem_id="search-bar", | |
) | |
with gr.Row(): | |
shown_columns = gr.CheckboxGroup( | |
choices=[ | |
c.name | |
for c in fields(AutoEvalColumn) | |
if not c.hidden and not c.never_hidden and not c.dummy | |
], | |
value=[ | |
c.name | |
for c in fields(AutoEvalColumn) | |
if c.displayed_by_default and not c.hidden and not c.never_hidden | |
], | |
label="Select columns to show", | |
elem_id="column-select", | |
interactive=True, | |
) | |
with gr.Row(): | |
hide_models = gr.CheckboxGroup( | |
label="Hide models", | |
choices=["Private or deleted", "Contains a merge/moerge", "Flagged", "MoE"], | |
value=["Private or deleted", "Contains a merge/moerge", "Flagged"], | |
interactive=True, | |
) | |
with gr.Column(min_width=320): | |
# with gr.Box(elem_id="box-filter"): | |
filter_columns_type = gr.CheckboxGroup( | |
label="Model types", | |
choices=[t.to_str() for t in ModelType], | |
value=[t.to_str() for t in ModelType], | |
interactive=True, | |
elem_id="filter-columns-type", | |
) | |
filter_columns_precision = gr.CheckboxGroup( | |
label="Precision", | |
choices=[i.value.name for i in Precision], | |
value=[i.value.name for i in Precision], | |
interactive=True, | |
elem_id="filter-columns-precision", | |
) | |
filter_columns_size = gr.CheckboxGroup( | |
label="Model sizes (in billions of parameters)", | |
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[ | |
[c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
+ shown_columns.value | |
+ [AutoEvalColumn.fullname.name] | |
], | |
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, | |
datatype=TYPES, | |
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[COLS], | |
headers=COLS, | |
datatype=TYPES, | |
visible=False, | |
) | |
search_bar.submit( | |
update_table, | |
[ | |
hidden_leaderboard_table_for_search, | |
shown_columns, | |
filter_columns_type, | |
filter_columns_precision, | |
filter_columns_size, | |
hide_models, | |
search_bar, | |
], | |
leaderboard_table, | |
) | |
# Define a hidden component that will trigger a reload only if a query parameter has been set | |
hidden_search_bar = gr.Textbox(value="", visible=False) | |
hidden_search_bar.change( | |
update_table, | |
[ | |
hidden_leaderboard_table_for_search, | |
shown_columns, | |
filter_columns_type, | |
filter_columns_precision, | |
filter_columns_size, | |
hide_models, | |
search_bar, | |
], | |
leaderboard_table, | |
) | |
# Check query parameter once at startup and update search bar + hidden component | |
demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar]) | |
for selector in [ | |
shown_columns, | |
filter_columns_type, | |
filter_columns_precision, | |
filter_columns_size, | |
hide_models, | |
]: | |
selector.change( | |
update_table, | |
[ | |
hidden_leaderboard_table_for_search, | |
shown_columns, | |
filter_columns_type, | |
filter_columns_precision, | |
filter_columns_size, | |
hide_models, | |
search_bar, | |
], | |
leaderboard_table, | |
queue=True, | |
) | |
with gr.TabItem("π Metrics through time", elem_id="llm-benchmark-tab-table", id=2): | |
with gr.Row(): | |
with gr.Column(): | |
plot_df = load_and_create_plots() | |
chart = create_metric_plot_obj( | |
plot_df, | |
[AutoEvalColumn.average.name], | |
title="Average of Top Scores and Human Baseline Over Time (from last update)", | |
) | |
gr.Plot(value=chart, min_width=500) | |
with gr.Column(): | |
plot_df = load_and_create_plots() | |
chart = create_metric_plot_obj( | |
plot_df, | |
BENCHMARK_COLS, | |
title="Top Scores and Human Baseline Over Time (from last update)", | |
) | |
gr.Plot(value=chart, min_width=500) | |
with gr.TabItem("π About", elem_id="llm-benchmark-tab-table", id=3): | |
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
with gr.TabItem("βFAQ", elem_id="llm-benchmark-tab-table", id=4): | |
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") | |
with gr.TabItem("π Submit ", elem_id="llm-benchmark-tab-table", id=5): | |
with gr.Column(): | |
with gr.Row(): | |
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
with gr.Row(): | |
gr.Markdown("# βοΈβ¨ 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 commit", placeholder="main") | |
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) | |
model_type = gr.Dropdown( | |
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], | |
label="Model type", | |
multiselect=False, | |
value=ModelType.FT.to_str(" : "), | |
interactive=True, | |
) | |
with gr.Column(): | |
precision = gr.Dropdown( | |
choices=[i.value.name for i in Precision if i != Precision.Unknown], | |
label="Precision", | |
multiselect=False, | |
value="float16", | |
interactive=True, | |
) | |
weight_type = gr.Dropdown( | |
choices=[i.value.name for i in WeightType], | |
label="Weights type", | |
multiselect=False, | |
value="Original", | |
interactive=True, | |
) | |
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") | |
with gr.Column(): | |
with gr.Accordion( | |
f"β 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, | |
row_count=5, | |
) | |
with gr.Accordion( | |
f"π 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, | |
row_count=5, | |
) | |
with gr.Accordion( | |
f"β³ 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, | |
row_count=5, | |
) | |
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(): | |
with gr.Accordion("π Citation", open=False): | |
citation_button = gr.Textbox( | |
value=CITATION_BUTTON_TEXT, | |
label=CITATION_BUTTON_LABEL, | |
lines=20, | |
elem_id="citation-button", | |
show_copy_button=True, | |
) | |
scheduler = BackgroundScheduler() | |
scheduler.add_job(restart_space, "interval", hours=3) # restarted every 3h | |
scheduler.add_job(update_dynamic_files, "interval", hours=2) # launched every 2 hour | |
scheduler.start() | |
demo.queue(default_concurrency_limit=40).launch() | |