#!/usr/bin/env python import os import datetime import socket from threading import Thread import gradio as gr import pandas as pd import time from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.display.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, LLM_BENCHMARKS_DETAILS, FAQ_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, TYPES, AutoEvalColumn, ModelType, InferenceFramework, fields, WeightType, Precision, ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, \ QUEUE_REPO, REPO_ID, RESULTS_REPO, DEBUG_QUEUE_REPO, DEBUG_RESULTS_REPO from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.submission.submit import add_new_eval from src.utils import get_dataset_summary_table def get_args(): import argparse parser = argparse.ArgumentParser(description="Run the LLM Leaderboard") parser.add_argument("--debug", action="store_true", help="Run in debug mode") return parser.parse_args() args = get_args() if args.debug: print("Running in debug mode") QUEUE_REPO = DEBUG_QUEUE_REPO RESULTS_REPO = DEBUG_RESULTS_REPO def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout): try: print(local_dir) snapshot_download( repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout ) except Exception as e: restart_space() def restart_space(): API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) def init_space(): dataset_df = get_dataset_summary_table(file_path="blog/Hallucination-Leaderboard-Summary.csv") if socket.gethostname() not in {"neuromancer"}: # sync model_type with open-llm-leaderboard ui_snapshot_download( repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 ) ui_snapshot_download( repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 ) raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, "", COLS, BENCHMARK_COLS) finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df( EVAL_REQUESTS_PATH, EVAL_COLS ) return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: list, size_query: list, query: str ): filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) filtered_df = filter_queries(query, filtered_df) 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] always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] dummy_col = [AutoEvalColumn.dummy.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] 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, filtered_df: pd.DataFrame): final_df = [] if query != "": queries = [q.strip() for q in query.split(";")] for _q in queries: _q = _q.strip() if _q != "": temp_filtered_df = search_table(filtered_df, _q) if len(temp_filtered_df) > 0: final_df.append(temp_filtered_df) if len(final_df) > 0: filtered_df = pd.concat(final_df) subset = [AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] filtered_df = filtered_df.drop_duplicates(subset=subset) return filtered_df def filter_models(df: pd.DataFrame, type_query: list, size_query: list, precision_query: list) -> pd.DataFrame: # Show all models filtered_df = df 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 shown_columns = None dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() leaderboard_df = original_df.copy() def update_leaderboard_table(): global leaderboard_df, shown_columns print("Updating leaderboard table") return leaderboard_df[ [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name] ] if not leaderboard_df.empty else leaderboard_df def update_hidden_leaderboard_table(): global original_df return original_df[COLS] if original_df.empty is False else original_df def update_dataset_table(): global dataset_df return dataset_df def update_finish_table(): global finished_eval_queue_df return finished_eval_queue_df def update_running_table(): global running_eval_queue_df return running_eval_queue_df def update_pending_table(): global pending_eval_queue_df return pending_eval_queue_df def update_finish_num(): global finished_eval_queue_df return len(finished_eval_queue_df) def update_running_num(): global running_eval_queue_df return len(running_eval_queue_df) def update_pending_num(): global pending_eval_queue_df return len(pending_eval_queue_df) # triggered only once at startup => read query parameter if it exists def load_query(request: gr.Request): query = request.query_params.get("query") or "" return query 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("open-moe-llm-leaderboard", elem_id="llm-benchmark-tab-table", id=0): with gr.Row(): with gr.Column(): with gr.Row(): search_bar = gr.Textbox( placeholder=" 🔍 Model search (separate multiple queries with `;`)", 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.Column(min_width=320): filter_columns_size = gr.CheckboxGroup( label="Inference frameworks", choices=[t.to_str() for t in InferenceFramework], value=[t.to_str() for t in InferenceFramework], interactive=True, elem_id="filter-columns-size", ) 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", # ) # breakpoint() leaderboard_table = gr.components.Dataframe( value=( leaderboard_df[ [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name] ] if leaderboard_df.empty is False else leaderboard_df ), 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, ) # column_widths=["2%", "20%"] # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=original_df[COLS] if original_df.empty is False else original_df, 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, search_bar, ], leaderboard_table, ) # Check query parameter once at startup and update search bar # demo.load(load_query, inputs=[], outputs=[search_bar]) for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, 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") dataset_table = gr.components.Dataframe( value=dataset_df, headers=list(dataset_df.columns), datatype=["str", "markdown", "str", "str", "str"], elem_id="dataset-table", interactive=False, visible=True, column_widths=["15%", "20%"], ) gr.Markdown(LLM_BENCHMARKS_DETAILS, elem_classes="markdown-text") gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") with gr.TabItem("Submit a model ", 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, row_count=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, row_count=5 ) with gr.Accordion(f"⏳ Scheduled 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 ) with gr.Row(): gr.Markdown("# Submit your model here", elem_classes="markdown-text") with gr.Row(): inference_framework = gr.Dropdown( choices=[t.to_str() for t in InferenceFramework], label="Inference framework", multiselect=False, value=None, interactive=True, ) 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=None, 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="float32", 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)") 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, inference_framework, ], submission_result, ) with gr.Row(): with gr.Accordion("Citing this leaderboard", 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=6) def launch_backend(): import subprocess from src.backend.envs import DEVICE if DEVICE not in {"cpu"}: _ = subprocess.run(["python", "backend-cli.py"]) Thread(target=periodic_init, daemon=True).start() # scheduler.add_job(launch_backend, "interval", seconds=120) if __name__ == "__main__": scheduler.start() block_launch()