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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.elo_leaderboard.load_results import get_elo_plots, get_elo_results_dicts | |
| 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, EloEvalColumn, fields, styled_error, styled_warning, styled_message | |
| from src.init import load_all_info_from_hub | |
| # clone / pull the lmeh eval data | |
| H4_TOKEN = os.environ.get("H4_TOKEN", None) | |
| LMEH_REPO = "HuggingFaceH4/lmeh_evaluations" | |
| HUMAN_EVAL_REPO = "HuggingFaceH4/scale-human-eval" | |
| GPT_4_EVAL_REPO = "HuggingFaceH4/open_llm_leaderboard_oai_evals" | |
| IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True)) | |
| ADD_PLOTS = False | |
| EVAL_REQUESTS_PATH = "auto_evals/eval_requests" | |
| api = HfApi() | |
| def restart_space(): | |
| api.restart_space( | |
| repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN | |
| ) | |
| auto_eval_repo, human_eval_repo, gpt_4_eval_repo, requested_models = load_all_info_from_hub(LMEH_REPO, HUMAN_EVAL_REPO, GPT_4_EVAL_REPO) | |
| 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.is_8bit.name) | |
| TYPES.insert(2, AutoEvalColumn.is_8bit.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]] | |
| ELO_COLS = [c.name for c in fields(EloEvalColumn)] | |
| ELO_TYPES = [c.type for c in fields(EloEvalColumn)] | |
| ELO_SORT_COL = EloEvalColumn.gpt4.name | |
| 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 auto_eval_repo: | |
| print("Pulling evaluation results for the leaderboard.") | |
| auto_eval_repo.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(): | |
| # todo @saylortwift: replace the repo by the one you created for the eval queue | |
| if auto_eval_repo: | |
| print("Pulling changes for the evaluation queue.") | |
| auto_eval_repo.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) | |
| else: | |
| # 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"] == "PENDING"] | |
| running_list = [e for e in all_evals if e["status"] == "RUNNING"] | |
| finished_list = [e for e in all_evals if e["status"] == "FINISHED"] | |
| df_pending = pd.DataFrame.from_records(pending_list) | |
| df_running = pd.DataFrame.from_records(running_list) | |
| df_finished = pd.DataFrame.from_records(finished_list) | |
| return df_finished[EVAL_COLS], df_running[EVAL_COLS], df_pending[EVAL_COLS] | |
| def get_elo_leaderboard(df_instruct, df_code_instruct, tie_allowed=False): | |
| if human_eval_repo: | |
| print("Pulling human_eval_repo changes") | |
| human_eval_repo.git_pull() | |
| all_data = get_elo_results_dicts(df_instruct, df_code_instruct, tie_allowed) | |
| dataframe = pd.DataFrame.from_records(all_data) | |
| dataframe = dataframe.sort_values(by=ELO_SORT_COL, ascending=False) | |
| dataframe = dataframe[ELO_COLS] | |
| return dataframe | |
| def get_elo_elements(): | |
| df_instruct = pd.read_json("human_evals/without_code.json") | |
| df_code_instruct = pd.read_json("human_evals/with_code.json") | |
| elo_leaderboard = get_elo_leaderboard( | |
| df_instruct, df_code_instruct, tie_allowed=False | |
| ) | |
| elo_leaderboard_with_tie_allowed = get_elo_leaderboard( | |
| df_instruct, df_code_instruct, tie_allowed=True | |
| ) | |
| plot_1, plot_2, plot_3, plot_4 = get_elo_plots( | |
| df_instruct, df_code_instruct, tie_allowed=False | |
| ) | |
| return ( | |
| elo_leaderboard, | |
| elo_leaderboard_with_tie_allowed, | |
| plot_1, | |
| plot_2, | |
| plot_3, | |
| plot_4, | |
| ) | |
| 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() | |
| ( | |
| elo_leaderboard, | |
| elo_leaderboard_with_tie_allowed, | |
| plot_1, | |
| plot_2, | |
| plot_3, | |
| plot_4, | |
| ) = get_elo_elements() | |
| 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("Could not get the model config from the hub.: \n", e) | |
| return False, "was not found on hub!" | |
| def add_new_eval( | |
| model: str, | |
| base_model: str, | |
| revision: str, | |
| is_8_bit_eval: bool, | |
| private: bool, | |
| is_delta_weight: bool, | |
| is_adapter_weight: bool, | |
| ): | |
| current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | |
| # check the model actually exists before adding the eval | |
| if revision == "": | |
| revision = "main" | |
| if is_delta_weight and is_adapter_weight: | |
| return styled_error("Cannot be both delta and adapter weight") | |
| if is_delta_weight or is_adapter_weight: | |
| 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}') | |
| 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, | |
| "8bit_eval": is_8_bit_eval, | |
| "is_delta_weight": is_delta_weight, | |
| "is_adapter_weight": is_adapter_weight, | |
| "status": "PENDING", | |
| "submitted_time": current_time, | |
| } | |
| 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}_{is_8_bit_eval}_{is_delta_weight}.json" | |
| # Check for duplicate submission | |
| if out_path.split("eval_requests/")[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, | |
| repo_id=LMEH_REPO, | |
| token=H4_TOKEN, | |
| repo_type="dataset", | |
| ) | |
| return styled_message("Your request has been submitted to the evaluation queue!") | |
| 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, query): | |
| filtered_df = df[df[AutoEvalColumn.dummy.name].str.contains(query, case=False)] | |
| return filtered_df | |
| 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) | |
| with gr.Row(): | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Column(): | |
| 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) | |
| with gr.Column(): | |
| with gr.Accordion("✨ CHANGELOG", open=False): | |
| changelog = gr.Markdown(CHANGELOG_TEXT, elem_id="changelog-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("📊 LLM Benchmarks", elem_id="llm-benchmark-tab-table", id=0): | |
| with gr.Column(): | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.Box(elem_id="search-bar-table-box"): | |
| search_bar = gr.Textbox( | |
| placeholder="🔍 Search your model and press ENTER...", | |
| show_label=False, | |
| elem_id="search-bar", | |
| ) | |
| with gr.Tabs(elem_classes="tab-buttons"): | |
| with gr.TabItem("Light View"): | |
| leaderboard_table_lite = gr.components.Dataframe( | |
| value=leaderboard_df[COLS_LITE], | |
| headers=COLS_LITE, | |
| datatype=TYPES_LITE, | |
| max_rows=None, | |
| elem_id="leaderboard-table-lite", | |
| ) | |
| with gr.TabItem("Extended Model View"): | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df, | |
| headers=COLS, | |
| datatype=TYPES, | |
| max_rows=None, | |
| elem_id="leaderboard-table", | |
| ) | |
| # 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, search_bar], | |
| leaderboard_table, | |
| ) | |
| # Dummy leaderboard for handling the case when the user uses backspace key | |
| hidden_leaderboard_table_for_search_lite = gr.components.Dataframe( | |
| value=original_df[COLS_LITE], | |
| headers=COLS_LITE, | |
| datatype=TYPES_LITE, | |
| max_rows=None, | |
| visible=False, | |
| ) | |
| search_bar.submit( | |
| search_table, | |
| [hidden_leaderboard_table_for_search_lite, search_bar], | |
| leaderboard_table_lite, | |
| ) | |
| with gr.Row(): | |
| gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
| with gr.Accordion("✅ Finished Evaluations", 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("🔄 Running Evaluation Queue", 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("⏳ Pending Evaluation Queue", 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(): | |
| refresh_button = gr.Button("Refresh") | |
| refresh_button.click( | |
| refresh, | |
| inputs=[], | |
| outputs=[ | |
| leaderboard_table, | |
| finished_eval_table, | |
| running_eval_table, | |
| pending_eval_table, | |
| ], | |
| ) | |
| with gr.Accordion("Submit a new model for evaluation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_name_textbox = gr.Textbox(label="Model name") | |
| revision_name_textbox = gr.Textbox( | |
| label="revision", placeholder="main" | |
| ) | |
| with gr.Column(): | |
| is_8bit_toggle = gr.Checkbox( | |
| False, label="8 bit eval", visible=not IS_PUBLIC | |
| ) | |
| private = gr.Checkbox( | |
| False, label="Private", visible=not IS_PUBLIC | |
| ) | |
| is_delta_weight = gr.Checkbox(False, label="Delta weights") | |
| is_adapter_weight = gr.Checkbox(False, label="Adapter weights") | |
| base_model_name_textbox = gr.Textbox( | |
| label="base model (for delta / adapter)" | |
| ) | |
| 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, | |
| is_8bit_toggle, | |
| private, | |
| is_delta_weight, | |
| is_adapter_weight, | |
| ], | |
| submission_result, | |
| ) | |
| with gr.TabItem( | |
| "🧑⚖️ Human & GPT-4 Evaluations 🤖", elem_id="human-gpt-tab-table", id=1 | |
| ): | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| gr.Markdown(HUMAN_GPT_EVAL_TEXT, elem_classes="markdown-text") | |
| with gr.Column(scale=1): | |
| gr.Image( | |
| "src/assets/scale-hf-logo.png", elem_id="scale-logo", show_label=False | |
| ) | |
| gr.Markdown("## No tie allowed") | |
| elo_leaderboard_table = gr.components.Dataframe( | |
| value=elo_leaderboard, | |
| headers=ELO_COLS, | |
| datatype=ELO_TYPES, | |
| max_rows=5, | |
| ) | |
| gr.Markdown("## Tie allowed*") | |
| elo_leaderboard_table_with_tie_allowed = gr.components.Dataframe( | |
| value=elo_leaderboard_with_tie_allowed, | |
| headers=ELO_COLS, | |
| datatype=ELO_TYPES, | |
| max_rows=5, | |
| ) | |
| gr.Markdown( | |
| "\* Results when the scores of 4 and 5 were treated as ties.", | |
| elem_classes="markdown-text", | |
| ) | |
| gr.Markdown( | |
| "Let us know in [this discussion](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/65) which models we should add!", | |
| elem_id="models-to-add-text", | |
| ) | |
| dummy = gr.Textbox(visible=False) | |
| demo.load( | |
| change_tab, | |
| dummy, | |
| tabs, | |
| _js=get_window_url_params, | |
| ) | |
| if ADD_PLOTS: | |
| with gr.Box(): | |
| visualization_title = gr.HTML(VISUALIZATION_TITLE) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(f"#### Figure 1: {PLOT_1_TITLE}") | |
| plot_1 = gr.Plot(plot_1, show_label=False) | |
| with gr.Column(): | |
| gr.Markdown(f"#### Figure 2: {PLOT_2_TITLE}") | |
| plot_2 = gr.Plot(plot_2, show_label=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(f"#### Figure 3: {PLOT_3_TITLE}") | |
| plot_3 = gr.Plot(plot_3, show_label=False) | |
| with gr.Column(): | |
| gr.Markdown(f"#### Figure 4: {PLOT_4_TITLE}") | |
| plot_4 = gr.Plot(plot_4, show_label=False) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=3600) | |
| scheduler.start() | |
| demo.queue(concurrency_count=40).launch() | |