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| import logging | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| import src.envs as envs | |
| from main_backend import PENDING_STATUS, RUNNING_STATUS, FINISHED_STATUS, FAILED_STATUS | |
| from src.backend import sort_queue | |
| from src.envs import EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, RESULTS_REPO | |
| import src.backend.manage_requests as manage_requests | |
| import socket | |
| import src.display.about as about | |
| from src.display.css_html_js import custom_css | |
| import src.display.utils as utils | |
| import src.populate as populate | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
| import src.submission.submit as submit | |
| import os | |
| import datetime | |
| import spacy_transformers | |
| import pprint | |
| import src.backend.run_eval_suite as run_eval_suite | |
| pp = pprint.PrettyPrinter(width=80) | |
| TOKEN = os.environ.get("H4_TOKEN", None) | |
| print("TOKEN", TOKEN) | |
| def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout): | |
| try: | |
| print("local", 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(): | |
| envs.API.restart_space(repo_id=envs.REPO_ID, token=TOKEN) | |
| def init_space(): | |
| #dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv') | |
| 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) | |
| original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, utils.COLS, utils.BENCHMARK_COLS) | |
| finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, utils.EVAL_COLS) | |
| return original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df | |
| original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() | |
| leaderboard_df = original_df.copy() | |
| def process_pending_evals(): | |
| current_pending_status = [PENDING_STATUS] | |
| print('_________________') | |
| manage_requests.check_completed_evals( | |
| api=envs.API, | |
| checked_status=RUNNING_STATUS, | |
| completed_status=FINISHED_STATUS, | |
| failed_status=FAILED_STATUS, | |
| hf_repo=envs.QUEUE_REPO, | |
| local_dir=envs.EVAL_REQUESTS_PATH_BACKEND, | |
| hf_repo_results=envs.RESULTS_REPO, | |
| local_dir_results=envs.EVAL_RESULTS_PATH_BACKEND | |
| ) | |
| logging.info("Checked completed evals") | |
| eval_requests = manage_requests.get_eval_requests( | |
| job_status=current_pending_status, | |
| hf_repo=envs.QUEUE_REPO, | |
| local_dir=envs.EVAL_REQUESTS_PATH_BACKEND | |
| ) | |
| logging.info("Got eval requests") | |
| eval_requests = sort_queue.sort_models_by_priority(api=envs.API, models=eval_requests) | |
| logging.info("Sorted eval requests") | |
| print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests") | |
| if len(eval_requests) == 0: | |
| print("No eval requests found. Exiting.") | |
| return | |
| import concurrent.futures | |
| def process_eval_request(eval_request): | |
| pp.pprint(eval_request) | |
| run_eval_suite.run_evaluation( | |
| eval_request=eval_request, | |
| local_dir=envs.EVAL_RESULTS_PATH_BACKEND, | |
| results_repo=envs.RESULTS_REPO, | |
| batch_size=1, | |
| device=envs.DEVICE, | |
| no_cache=True, | |
| need_check=False, | |
| write_results=False | |
| ) | |
| logging.info(f"Eval finished for model {eval_request.model}, now setting status to finished") | |
| # Update the status to FINISHED | |
| manage_requests.set_eval_request( | |
| api=envs.API, | |
| eval_request=eval_request, | |
| new_status=FINISHED_STATUS, | |
| hf_repo=envs.QUEUE_REPO, | |
| local_dir=envs.EVAL_REQUESTS_PATH_BACKEND | |
| ) | |
| # 定义线程池的数量 | |
| max_workers = 5 # 你可以根据你的需求设置合适的数量 | |
| # 使用 ThreadPoolExecutor 来并行执行多个 eval_request | |
| with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: | |
| futures = [executor.submit(process_eval_request, eval_request) for eval_request in eval_requests] | |
| # 等待所有任务完成 | |
| concurrent.futures.wait(futures) | |
| # for eval_request in eval_requests: | |
| # pp.pprint(eval_request) | |
| # run_eval_suite.run_evaluation( | |
| # eval_request=eval_request, | |
| # local_dir=envs.EVAL_RESULTS_PATH_BACKEND, | |
| # results_repo=envs.RESULTS_REPO, | |
| # batch_size=1, | |
| # device=envs.DEVICE, | |
| # no_cache=True, | |
| # need_check= False, | |
| # write_results= False | |
| # ) | |
| # logging.info(f"Eval finished for model {eval_request.model}, now setting status to finished") | |
| # | |
| # # Update the status to FINISHED | |
| # manage_requests.set_eval_request( | |
| # api=envs.API, | |
| # eval_request=eval_request, | |
| # new_status=FINISHED_STATUS, | |
| # hf_repo=envs.QUEUE_REPO, | |
| # local_dir=envs.EVAL_REQUESTS_PATH_BACKEND | |
| # ) | |
| # Searching and filtering | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| columns: list, | |
| #type_query: list, | |
| # precision_query: str, | |
| # size_query: list, | |
| # show_deleted: bool, | |
| query: str, | |
| ): | |
| # filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) | |
| filtered_df = filter_models(hidden_df) | |
| 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[utils.AutoEvalColumn.dummy.name].str.contains(query, case=False))] | |
| def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: | |
| always_here_cols = [ | |
| #utils.AutoEvalColumn.model_type_symbol.name, | |
| utils.AutoEvalColumn.model.name, | |
| ] | |
| # We use COLS to maintain sorting | |
| filtered_df = df[ | |
| always_here_cols + [c for c in utils.COLS if c in df.columns and c in columns] + [utils.AutoEvalColumn.dummy.name] | |
| ] | |
| return filtered_df | |
| def filter_queries(query: str, filtered_df: pd.DataFrame) -> 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) | |
| filtered_df = filtered_df.drop_duplicates( | |
| subset=[utils.AutoEvalColumn.model.name, utils.AutoEvalColumn.precision.name, utils.AutoEvalColumn.revision.name] | |
| ) | |
| return filtered_df | |
| def filter_models( | |
| # df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool | |
| df: pd.DataFrame | |
| ) -> pd.DataFrame: | |
| # Show all models | |
| # if show_deleted: | |
| # filtered_df = df | |
| # else: # Show only still on the hub models | |
| # filtered_df = df[df[utils.AutoEvalColumn.still_on_hub.name]] | |
| filtered_df = df | |
| # type_emoji = [t[0] for t in type_query] | |
| #filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] | |
| # filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.precision.name].isin(precision_query + ["None"])] | |
| # | |
| # numeric_interval = pd.IntervalIndex(sorted([utils.NUMERIC_INTERVALS[s] for s in size_query])) | |
| # params_column = pd.to_numeric(df[utils.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 | |
| try: | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.HTML(about.TITLE) | |
| gr.Markdown(about.INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| def display_radar_chart(): | |
| return """ | |
| <iframe src="https://crimson-bernette-67.tiiny.site/"></iframe> | |
| """ | |
| gr.HTML(display_radar_chart()) | |
| 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 for your model (separate multiple queries with `;`) and press ENTER...", | |
| show_label=False, | |
| elem_id="search-bar", | |
| ) | |
| with gr.Row(): | |
| shown_columns = gr.CheckboxGroup( | |
| choices=[ | |
| c.name | |
| for c in utils.fields(utils.AutoEvalColumn) | |
| if not c.hidden and not c.never_hidden and not c.dummy | |
| ], | |
| value=[ | |
| c.name | |
| for c in utils.fields(utils.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(): | |
| # deleted_models_visibility = gr.Checkbox( | |
| # value=False, label="Show gated/private/deleted models", 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 utils.ModelType], | |
| # value=[t.to_str() for t in utils.ModelType], | |
| # interactive=True, | |
| # elem_id="filter-columns-type", | |
| # ) | |
| # filter_columns_precision = gr.CheckboxGroup( | |
| # label="Precision", | |
| # choices=[i.value.name for i in utils.Precision], | |
| # value=[i.value.name for i in utils.Precision], | |
| # interactive=True, | |
| # elem_id="filter-columns-precision", | |
| # ) | |
| # filter_columns_size = gr.CheckboxGroup( | |
| # label="Model sizes (in billions of parameters)", | |
| # choices=list(utils.NUMERIC_INTERVALS.keys()), | |
| # value=list(utils.NUMERIC_INTERVALS.keys()), | |
| # interactive=True, | |
| # elem_id="filter-columns-size", | |
| # ) | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df[ | |
| [c.name for c in utils.fields(utils.AutoEvalColumn) if c.never_hidden] | |
| + shown_columns.value | |
| + [utils.AutoEvalColumn.dummy.name] | |
| ].sort_values(by="Overall Humanlike %", ascending=False), | |
| headers=[c.name for c in utils.fields(utils.AutoEvalColumn) if c.never_hidden] + shown_columns.value, | |
| datatype=utils.TYPES, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| visible=True, | |
| column_widths=["33%", "16.6%"] | |
| ) | |
| # Dummy leaderboard for handling the case when the user uses backspace key | |
| hidden_leaderboard_table_for_search = gr.components.Dataframe( | |
| value=original_df[utils.COLS], | |
| headers=utils.COLS, | |
| datatype=utils.TYPES, | |
| visible=False, | |
| ) | |
| search_bar.submit( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| #filter_columns_type, | |
| #filter_columns_precision, | |
| #filter_columns_size, | |
| # deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| # for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]: | |
| for selector in [shown_columns]: | |
| selector.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| #filter_columns_type, | |
| # filter_columns_precision, | |
| # filter_columns_size, | |
| # deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): | |
| gr.Markdown(about.LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown(about.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=utils.EVAL_COLS, | |
| datatype=utils.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=utils.EVAL_COLS, | |
| datatype=utils.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=utils.EVAL_COLS, | |
| datatype=utils.EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| 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") | |
| model_type = gr.Dropdown( | |
| choices=[t.to_str(" : ") for t in utils.ModelType if t != utils.ModelType.Unknown], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| precision = gr.Dropdown( | |
| choices=[i.value.name for i in utils.Precision if i != utils.Precision.Unknown], | |
| label="Precision", | |
| multiselect=False, | |
| value="float16", | |
| interactive=True, | |
| ) | |
| weight_type = gr.Dropdown( | |
| choices=[i.value.name for i in utils.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( | |
| submit.add_new_eval, | |
| [ | |
| model_name_textbox, | |
| base_model_name_textbox, | |
| revision_name_textbox, | |
| precision, | |
| weight_type, | |
| model_type, | |
| ], | |
| submission_result, | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=about.CITATION_BUTTON_TEXT, | |
| label=about.CITATION_BUTTON_LABEL, | |
| lines=20, | |
| elem_id="citation-button", | |
| show_copy_button=True, | |
| ) | |
| except Exception as e: | |
| print(e) | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = populate.get_evaluation_queue_df(envs.EVAL_REQUESTS_PATH, utils.EVAL_COLS) | |
| def background_init_and_process(): | |
| global original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df | |
| original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() | |
| process_pending_evals() | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(background_init_and_process, 'date', run_date=datetime.datetime.now()) # 立即执行 | |
| scheduler.add_job(restart_space, "interval", seconds=1720000) | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() |