Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| import os | |
| import pandas as pd | |
| import logging | |
| import time | |
| import gradio as gr | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns | |
| 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 | |
| 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): | |
| leaderboard = Leaderboard( | |
| value=leaderboard_df, | |
| datatype=[c.type for c in fields(AutoEvalColumn)], | |
| select_columns=SelectColumns( | |
| default_selection=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if c.displayed_by_default | |
| ], | |
| cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.dummy], | |
| label="Select Columns to Display:", | |
| ), | |
| search_columns=[ | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.fullname.name, | |
| AutoEvalColumn.license.name | |
| ], | |
| hide_columns=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if c.hidden | |
| ], | |
| filter_columns=[ | |
| ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"), | |
| ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), | |
| ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=150, label="Select the number of parameters (B)"), | |
| ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Private or deleted", default=True), | |
| ColumnFilter(AutoEvalColumn.merged.name, type="boolean", label="Contains a merge/moerge", default=True), | |
| ColumnFilter(AutoEvalColumn.moe.name, type="boolean", label="MoE", default=False), | |
| ColumnFilter(AutoEvalColumn.not_flagged.name, type="boolean", label="Flagged", default=True), | |
| ], | |
| bool_checkboxgroup_label="Hide models" | |
| ) | |
| 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() |