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import os |
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import datetime |
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import socket |
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import gradio as gr |
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import pandas as pd |
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from apscheduler.schedulers.background import BackgroundScheduler |
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from huggingface_hub import snapshot_download |
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from src.display.about import ( |
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CITATION_BUTTON_LABEL, |
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CITATION_BUTTON_TEXT, |
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EVALUATION_QUEUE_TEXT, |
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INTRODUCTION_TEXT, |
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LLM_BENCHMARKS_TEXT, |
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LLM_BENCHMARKS_DETAILS, |
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FAQ_TEXT, |
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TITLE, |
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) |
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from src.display.css_html_js import custom_css |
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from src.display.utils import ( |
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BENCHMARK_COLS, |
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COLS, |
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EVAL_COLS, |
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EVAL_TYPES, |
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TYPES, |
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AutoEvalColumn, |
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ModelType, |
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InferenceFramework, |
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fields, |
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WeightType, |
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Precision, |
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) |
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from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO |
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from src.populate import get_evaluation_queue_df, get_leaderboard_df |
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from src.submission.submit import add_new_eval |
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from src.utils import get_dataset_summary_table |
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def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout): |
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try: |
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print(local_dir) |
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snapshot_download( |
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repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout |
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) |
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except Exception as e: |
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restart_space() |
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def restart_space(): |
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API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) |
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def init_space(): |
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dataset_df = get_dataset_summary_table(file_path="blog/Hallucination-Leaderboard-Summary.csv") |
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if socket.gethostname() not in {"neuromancer"}: |
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ui_snapshot_download( |
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repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 |
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) |
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ui_snapshot_download( |
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repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 |
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) |
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raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, "", COLS, BENCHMARK_COLS) |
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finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df( |
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EVAL_REQUESTS_PATH, EVAL_COLS |
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) |
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return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df |
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dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() |
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leaderboard_df = original_df.copy() |
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def update_table( |
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hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: list, size_query: list, query: str |
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): |
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filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) |
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filtered_df = filter_queries(query, filtered_df) |
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df = select_columns(filtered_df, columns) |
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return df |
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def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: |
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return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] |
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def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: |
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always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] |
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dummy_col = [AutoEvalColumn.dummy.name] |
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filtered_df = df[ |
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always_here_cols |
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+ [c for c in COLS if c in df.columns and c in columns] |
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+ dummy_col |
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] |
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return filtered_df |
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def filter_queries(query: str, filtered_df: pd.DataFrame): |
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final_df = [] |
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if query != "": |
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queries = [q.strip() for q in query.split(";")] |
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for _q in queries: |
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_q = _q.strip() |
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if _q != "": |
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temp_filtered_df = search_table(filtered_df, _q) |
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if len(temp_filtered_df) > 0: |
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final_df.append(temp_filtered_df) |
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if len(final_df) > 0: |
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filtered_df = pd.concat(final_df) |
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subset = [AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] |
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filtered_df = filtered_df.drop_duplicates(subset=subset) |
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return filtered_df |
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def filter_models(df: pd.DataFrame, type_query: list, size_query: list, precision_query: list) -> pd.DataFrame: |
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filtered_df = df |
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type_emoji = [t[0] for t in type_query] |
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filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] |
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filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])] |
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return filtered_df |
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def load_query(request: gr.Request): |
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query = request.query_params.get("query") or "" |
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return query |
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demo = gr.Blocks(css=custom_css) |
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with demo: |
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gr.HTML(TITLE) |
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gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") |
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with gr.Tabs(elem_classes="tab-buttons") as tabs: |
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with gr.TabItem("open-moe-llm-leaderboard", elem_id="llm-benchmark-tab-table", id=0): |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Row(): |
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search_bar = gr.Textbox( |
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placeholder=" 🔍 Model search (separate multiple queries with `;`)", |
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show_label=False, |
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elem_id="search-bar", |
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) |
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with gr.Row(): |
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shown_columns = gr.CheckboxGroup( |
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choices=[ |
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c.name |
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for c in fields(AutoEvalColumn) |
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if not c.hidden and not c.never_hidden and not c.dummy |
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], |
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value=[ |
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c.name |
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for c in fields(AutoEvalColumn) |
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if c.displayed_by_default and not c.hidden and not c.never_hidden |
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], |
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label="Select columns to show", |
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elem_id="column-select", |
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interactive=True, |
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) |
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with gr.Column(min_width=320): |
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filter_columns_size = gr.CheckboxGroup( |
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label="Inference frameworks", |
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choices=[t.to_str() for t in InferenceFramework], |
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value=[t.to_str() for t in InferenceFramework], |
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interactive=True, |
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elem_id="filter-columns-size", |
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) |
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filter_columns_type = gr.CheckboxGroup( |
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label="Model types", |
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choices=[t.to_str() for t in ModelType], |
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value=[t.to_str() for t in ModelType], |
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interactive=True, |
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elem_id="filter-columns-type", |
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) |
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filter_columns_precision = gr.CheckboxGroup( |
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label="Precision", |
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choices=[i.value.name for i in Precision], |
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value=[i.value.name for i in Precision], |
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interactive=True, |
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elem_id="filter-columns-precision", |
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) |
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leaderboard_table = gr.components.Dataframe( |
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value=( |
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leaderboard_df[ |
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[c.name for c in fields(AutoEvalColumn) if c.never_hidden] |
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+ shown_columns.value |
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+ [AutoEvalColumn.dummy.name] |
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] |
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if leaderboard_df.empty is False |
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else leaderboard_df |
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), |
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headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, |
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datatype=TYPES, |
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elem_id="leaderboard-table", |
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interactive=False, |
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visible=True, |
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) |
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hidden_leaderboard_table_for_search = gr.components.Dataframe( |
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value=original_df[COLS] if original_df.empty is False else original_df, |
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headers=COLS, |
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datatype=TYPES, |
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visible=False, |
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) |
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search_bar.submit( |
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update_table, |
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[ |
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hidden_leaderboard_table_for_search, |
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shown_columns, |
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filter_columns_type, |
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filter_columns_precision, |
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filter_columns_size, |
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search_bar, |
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], |
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leaderboard_table, |
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) |
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for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]: |
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selector.change( |
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update_table, |
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[ |
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hidden_leaderboard_table_for_search, |
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shown_columns, |
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filter_columns_type, |
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filter_columns_precision, |
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filter_columns_size, |
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search_bar, |
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], |
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leaderboard_table, |
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queue=True, |
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) |
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with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2): |
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") |
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dataset_table = gr.components.Dataframe( |
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value=dataset_df, |
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headers=list(dataset_df.columns), |
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datatype=["str", "markdown", "str", "str", "str"], |
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elem_id="dataset-table", |
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interactive=False, |
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visible=True, |
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column_widths=["15%", "20%"], |
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) |
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gr.Markdown(LLM_BENCHMARKS_DETAILS, elem_classes="markdown-text") |
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gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") |
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with gr.TabItem("Submit a model ", elem_id="llm-benchmark-tab-table", id=3): |
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with gr.Column(): |
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with gr.Row(): |
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gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") |
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with gr.Column(): |
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with gr.Accordion(f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", open=False): |
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with gr.Row(): |
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finished_eval_table = gr.components.Dataframe( |
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value=finished_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5 |
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) |
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with gr.Accordion(f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", open=False): |
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with gr.Row(): |
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running_eval_table = gr.components.Dataframe( |
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value=running_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5 |
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) |
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with gr.Accordion(f"⏳ Scheduled Evaluation Queue ({len(pending_eval_queue_df)})", open=False): |
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with gr.Row(): |
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pending_eval_table = gr.components.Dataframe( |
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value=pending_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5 |
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) |
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with gr.Row(): |
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gr.Markdown("# Submit your model here", elem_classes="markdown-text") |
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with gr.Row(): |
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inference_framework = gr.Dropdown( |
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choices=[t.to_str() for t in InferenceFramework], |
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label="Inference framework", |
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multiselect=False, |
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value=None, |
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interactive=True, |
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) |
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with gr.Row(): |
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with gr.Column(): |
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model_name_textbox = gr.Textbox(label="Model name") |
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revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") |
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private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) |
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model_type = gr.Dropdown( |
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choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], |
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label="Model type", |
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multiselect=False, |
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value=None, |
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interactive=True, |
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) |
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with gr.Column(): |
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precision = gr.Dropdown( |
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choices=[i.value.name for i in Precision if i != Precision.Unknown], |
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label="Precision", |
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multiselect=False, |
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value="float32", |
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interactive=True, |
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) |
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weight_type = gr.Dropdown( |
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choices=[i.value.name for i in WeightType], |
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label="Weights type", |
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multiselect=False, |
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value="Original", |
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interactive=True, |
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) |
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base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") |
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submit_button = gr.Button("Submit Eval") |
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submission_result = gr.Markdown() |
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submit_button.click( |
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add_new_eval, |
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[ |
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model_name_textbox, |
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base_model_name_textbox, |
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revision_name_textbox, |
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precision, |
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private, |
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weight_type, |
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model_type, |
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inference_framework, |
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], |
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submission_result, |
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) |
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with gr.Row(): |
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with gr.Accordion("Citing this leaderboard", open=False): |
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citation_button = gr.Textbox( |
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value=CITATION_BUTTON_TEXT, |
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label=CITATION_BUTTON_LABEL, |
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lines=20, |
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elem_id="citation-button", |
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show_copy_button=True, |
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) |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(restart_space, "interval", seconds=6 * 60 * 60) |
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def launch_backend(): |
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import subprocess |
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from src.backend.envs import DEVICE |
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if DEVICE not in {"cpu"}: |
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_ = subprocess.run(["python", "backend-cli.py"]) |
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scheduler.start() |
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demo.queue(default_concurrency_limit=40).launch() |
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