Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| import os | |
| 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 | |
| # 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 download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3): | |
| """Attempt to download dataset with retries.""" | |
| attempt = 0 | |
| while attempt < max_attempts: | |
| try: | |
| print(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, | |
| ) | |
| return | |
| except Exception as e: | |
| logging.error(f"Error downloading {repo_id}: {e}") | |
| attempt += 1 | |
| if attempt == max_attempts: | |
| restart_space() | |
| 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 | |
| download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH) | |
| download_dataset(DYNAMIC_INFO_REPO, DYNAMIC_INFO_PATH) | |
| download_dataset(RESULTS_REPO, EVAL_RESULTS_PATH) | |
| # 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("🏅 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 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("📈 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("📝 About", elem_id="llm-benchmark-tab-table", id=3): | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4): | |
| gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("🚀 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("# ✉️✨ 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"✅ 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"⏳ 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("📙 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() | |