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| import json | |
| import os | |
| from datetime import datetime, timezone | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import HfApi | |
| from transformers import AutoConfig | |
| from src.auto_leaderboard.get_model_metadata import apply_metadata | |
| from src.assets.text_content import * | |
| from src.auto_leaderboard.load_results import get_eval_results_dicts, make_clickable_model | |
| from src.assets.hardcoded_evals import gpt4_values, gpt35_values, baseline | |
| from src.assets.css_html_js import custom_css, get_window_url_params | |
| from src.utils_display import AutoEvalColumn, EvalQueueColumn, fields, styled_error, styled_warning, styled_message | |
| from src.init import get_all_requested_models, load_all_info_from_hub | |
| # clone / pull the lmeh eval data | |
| H4_TOKEN = os.environ.get("H4_TOKEN", None) | |
| QUEUE_REPO = "open-llm-leaderboard/requests" | |
| RESULTS_REPO = "open-llm-leaderboard/results" | |
| PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests" | |
| PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results" | |
| IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True)) | |
| EVAL_REQUESTS_PATH = "eval-queue" | |
| EVAL_RESULTS_PATH = "eval-results" | |
| EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private" | |
| EVAL_RESULTS_PATH_PRIVATE = "eval-results-private" | |
| api = HfApi() | |
| def restart_space(): | |
| api.restart_space( | |
| repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN | |
| ) | |
| eval_queue, requested_models, eval_results = load_all_info_from_hub(QUEUE_REPO, RESULTS_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH) | |
| if not IS_PUBLIC: | |
| eval_queue_private, requested_models_private, eval_results_private = load_all_info_from_hub(PRIVATE_QUEUE_REPO, PRIVATE_RESULTS_REPO, EVAL_REQUESTS_PATH_PRIVATE, EVAL_RESULTS_PATH_PRIVATE) | |
| else: | |
| eval_queue_private, eval_results_private = None, None | |
| COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] | |
| TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden] | |
| COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden] | |
| TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden] | |
| if not IS_PUBLIC: | |
| COLS.insert(2, AutoEvalColumn.precision.name) | |
| TYPES.insert(2, AutoEvalColumn.precision.type) | |
| EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] | |
| EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] | |
| BENCHMARK_COLS = [c.name for c in [AutoEvalColumn.arc, AutoEvalColumn.hellaswag, AutoEvalColumn.mmlu, AutoEvalColumn.truthfulqa]] | |
| def has_no_nan_values(df, columns): | |
| return df[columns].notna().all(axis=1) | |
| def has_nan_values(df, columns): | |
| return df[columns].isna().any(axis=1) | |
| def get_leaderboard_df(): | |
| if eval_results: | |
| print("Pulling evaluation results for the leaderboard.") | |
| eval_results.git_pull() | |
| if eval_results_private: | |
| print("Pulling evaluation results for the leaderboard.") | |
| eval_results_private.git_pull() | |
| all_data = get_eval_results_dicts(IS_PUBLIC) | |
| if not IS_PUBLIC: | |
| all_data.append(gpt4_values) | |
| all_data.append(gpt35_values) | |
| all_data.append(baseline) | |
| apply_metadata(all_data) # Populate model type based on known hardcoded values in `metadata.py` | |
| df = pd.DataFrame.from_records(all_data) | |
| df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) | |
| df = df[COLS] | |
| # filter out if any of the benchmarks have not been produced | |
| df = df[has_no_nan_values(df, BENCHMARK_COLS)] | |
| return df | |
| def get_evaluation_queue_df(): | |
| if eval_queue: | |
| print("Pulling changes for the evaluation queue.") | |
| eval_queue.git_pull() | |
| if eval_queue_private: | |
| print("Pulling changes for the evaluation queue.") | |
| eval_queue_private.git_pull() | |
| entries = [ | |
| entry | |
| for entry in os.listdir(EVAL_REQUESTS_PATH) | |
| if not entry.startswith(".") | |
| ] | |
| all_evals = [] | |
| for entry in entries: | |
| if ".json" in entry: | |
| file_path = os.path.join(EVAL_REQUESTS_PATH, entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| data["# params"] = "unknown" | |
| data["model"] = make_clickable_model(data["model"]) | |
| data["revision"] = data.get("revision", "main") | |
| all_evals.append(data) | |
| elif ".md" not in entry: | |
| # this is a folder | |
| sub_entries = [ | |
| e | |
| for e in os.listdir(f"{EVAL_REQUESTS_PATH}/{entry}") | |
| if not e.startswith(".") | |
| ] | |
| for sub_entry in sub_entries: | |
| file_path = os.path.join(EVAL_REQUESTS_PATH, entry, sub_entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| # data["# params"] = get_n_params(data["model"]) | |
| data["model"] = make_clickable_model(data["model"]) | |
| all_evals.append(data) | |
| pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]] | |
| running_list = [e for e in all_evals if e["status"] == "RUNNING"] | |
| finished_list = [e for e in all_evals if e["status"].startswith("FINISHED")] | |
| df_pending = pd.DataFrame.from_records(pending_list, columns=EVAL_COLS) | |
| df_running = pd.DataFrame.from_records(running_list, columns=EVAL_COLS) | |
| df_finished = pd.DataFrame.from_records(finished_list, columns=EVAL_COLS) | |
| return df_finished[EVAL_COLS], df_running[EVAL_COLS], df_pending[EVAL_COLS] | |
| original_df = get_leaderboard_df() | |
| leaderboard_df = original_df.copy() | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df() | |
| def is_model_on_hub(model_name, revision) -> bool: | |
| try: | |
| AutoConfig.from_pretrained(model_name, revision=revision) | |
| return True, None | |
| except ValueError as e: | |
| return False, "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard." | |
| except Exception as e: | |
| print(f"Could not get the model config from the hub.: {e}") | |
| return False, "was not found on hub!" | |
| def add_new_eval( | |
| model: str, | |
| base_model: str, | |
| revision: str, | |
| precision: str, | |
| private: bool, | |
| weight_type: str, | |
| model_type: str, | |
| ): | |
| precision = precision.split(" ")[0] | |
| current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | |
| if model_type is None or model_type == "": | |
| return styled_error("Please select a model type.") | |
| # check the model actually exists before adding the eval | |
| if revision == "": | |
| revision = "main" | |
| if weight_type in ["Delta", "Adapter"]: | |
| base_model_on_hub, error = is_model_on_hub(base_model, revision) | |
| if not base_model_on_hub: | |
| return styled_error(f'Base model "{base_model}" {error}') | |
| if not weight_type == "Adapter": | |
| model_on_hub, error = is_model_on_hub(model, revision) | |
| if not model_on_hub: | |
| return styled_error(f'Model "{model}" {error}') | |
| print("adding new eval") | |
| eval_entry = { | |
| "model": model, | |
| "base_model": base_model, | |
| "revision": revision, | |
| "private": private, | |
| "precision": precision, | |
| "weight_type": weight_type, | |
| "status": "PENDING", | |
| "submitted_time": current_time, | |
| "model_type": model_type, | |
| } | |
| user_name = "" | |
| model_path = model | |
| if "/" in model: | |
| user_name = model.split("/")[0] | |
| model_path = model.split("/")[1] | |
| OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}" | |
| os.makedirs(OUT_DIR, exist_ok=True) | |
| out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json" | |
| # Check for duplicate submission | |
| if out_path.split("eval-queue/")[1].lower() in requested_models: | |
| return styled_warning("This model has been already submitted.") | |
| with open(out_path, "w") as f: | |
| f.write(json.dumps(eval_entry)) | |
| api.upload_file( | |
| path_or_fileobj=out_path, | |
| path_in_repo=out_path.split("eval-queue/")[1], | |
| repo_id=QUEUE_REPO, | |
| token=H4_TOKEN, | |
| repo_type="dataset", | |
| commit_message=f"Add {model} to eval queue", | |
| ) | |
| # remove the local file | |
| os.remove(out_path) | |
| return styled_message("Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list.") | |
| def refresh(): | |
| leaderboard_df = get_leaderboard_df() | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df() | |
| return ( | |
| leaderboard_df, | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) | |
| def search_table(df, leaderboard_table, query): | |
| if AutoEvalColumn.model_type.name in leaderboard_table.columns: | |
| filtered_df = df[ | |
| (df[AutoEvalColumn.dummy.name].str.contains(query, case=False)) | |
| | (df[AutoEvalColumn.model_type.name].str.contains(query, case=False)) | |
| ] | |
| else: | |
| filtered_df = df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] | |
| return filtered_df[leaderboard_table.columns] | |
| def select_columns(df, columns): | |
| always_here_cols = [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.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]] | |
| return filtered_df | |
| #TODO allow this to filter by values of any columns | |
| def filter_items(df, leaderboard_table, query): | |
| if query == "all": | |
| return df[leaderboard_table.columns] | |
| else: | |
| query = query[0] #take only the emoji character | |
| if AutoEvalColumn.model_type_symbol.name in leaderboard_table.columns: | |
| filtered_df = df[(df[AutoEvalColumn.model_type_symbol.name] == query)] | |
| else: | |
| return leaderboard_table.columns | |
| return filtered_df[leaderboard_table.columns] | |
| def change_tab(query_param): | |
| query_param = query_param.replace("'", '"') | |
| query_param = json.loads(query_param) | |
| if ( | |
| isinstance(query_param, dict) | |
| and "tab" in query_param | |
| and query_param["tab"] == "evaluation" | |
| ): | |
| return gr.Tabs.update(selected=1) | |
| else: | |
| return gr.Tabs.update(selected=0) | |
| 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(): | |
| shown_columns = gr.CheckboxGroup( | |
| choices = [c for c in COLS if c not in [AutoEvalColumn.dummy.name, AutoEvalColumn.model.name, AutoEvalColumn.model_type_symbol.name]], | |
| value = [c for c in COLS_LITE if c not in [AutoEvalColumn.dummy.name, AutoEvalColumn.model.name, AutoEvalColumn.model_type_symbol.name]], | |
| label="Select columns to show", | |
| elem_id="column-select", | |
| interactive=True, | |
| ) | |
| with gr.Column(min_width=320): | |
| search_bar = gr.Textbox( | |
| placeholder="🔍 Search for your model and press ENTER...", | |
| show_label=False, | |
| elem_id="search-bar", | |
| ) | |
| filter_columns = gr.Radio( | |
| label="⏚ Filter model types", | |
| choices = [ | |
| "all", | |
| ModelType.PT.to_str(), | |
| ModelType.FT.to_str(), | |
| ModelType.IFT.to_str(), | |
| ModelType.RL.to_str(), | |
| ], | |
| value="all", | |
| elem_id="filter-columns" | |
| ) | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df[[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value+ [AutoEvalColumn.dummy.name]], | |
| headers=[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value + [AutoEvalColumn.dummy.name], | |
| datatype=TYPES, | |
| max_rows=None, | |
| 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, | |
| headers=COLS, | |
| datatype=TYPES, | |
| max_rows=None, | |
| visible=False, | |
| ) | |
| search_bar.submit( | |
| search_table, | |
| [hidden_leaderboard_table_for_search, leaderboard_table, search_bar], | |
| leaderboard_table, | |
| ) | |
| shown_columns.change(select_columns, [hidden_leaderboard_table_for_search, shown_columns], leaderboard_table) | |
| filter_columns.change(filter_items, [hidden_leaderboard_table_for_search, leaderboard_table, filter_columns], leaderboard_table) | |
| with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): | |
| gr.Markdown(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(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, | |
| max_rows=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, | |
| max_rows=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, | |
| max_rows=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", placeholder="main" | |
| ) | |
| private = gr.Checkbox( | |
| False, label="Private", visible=not IS_PUBLIC | |
| ) | |
| model_type = gr.Dropdown( | |
| choices=[ | |
| ModelType.PT.to_str(" : "), | |
| ModelType.FT.to_str(" : "), | |
| ModelType.IFT.to_str(" : "), | |
| ModelType.RL.to_str(" : "), | |
| ], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| precision = gr.Dropdown( | |
| choices=["float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)"], | |
| label="Precision", | |
| multiselect=False, | |
| value="float16", | |
| interactive=True, | |
| ) | |
| weight_type = gr.Dropdown( | |
| choices=["Original", "Delta", "Adapter"], | |
| 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 | |
| ], | |
| submission_result, | |
| ) | |
| with gr.Row(): | |
| refresh_button = gr.Button("Refresh") | |
| refresh_button.click( | |
| refresh, | |
| inputs=[], | |
| outputs=[ | |
| leaderboard_table, | |
| finished_eval_table, | |
| running_eval_table, | |
| pending_eval_table, | |
| ], | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| elem_id="citation-button", | |
| ).style(show_copy_button=True) | |
| dummy = gr.Textbox(visible=False) | |
| demo.load( | |
| change_tab, | |
| dummy, | |
| tabs, | |
| _js=get_window_url_params, | |
| ) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=3600) | |
| scheduler.start() | |
| demo.queue(concurrency_count=40).launch() | |