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| import os | |
| import json | |
| import numpy as np | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from content import * | |
| from huggingface_hub import Repository, HfApi | |
| from transformers import AutoConfig | |
| from utils import get_eval_results_dicts, make_clickable_model | |
| # clone / pull the lmeh eval data | |
| H4_TOKEN = os.environ.get("H4_TOKEN", None) | |
| LMEH_REPO = "HuggingFaceH4/lmeh_evaluations" | |
| IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", None)) | |
| api = HfApi() | |
| def restart_space(): | |
| api.restart_space(repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN) | |
| def get_all_requested_models(requested_models_dir): | |
| depth = 1 | |
| file_names = [] | |
| for root, dirs, files in os.walk(requested_models_dir): | |
| current_depth = root.count(os.sep) - requested_models_dir.count(os.sep) | |
| if current_depth == depth: | |
| file_names.extend([os.path.join(root, file) for file in files]) | |
| return set([file_name.lower().split("./evals/")[1] for file_name in file_names]) | |
| repo = None | |
| requested_models = None | |
| if H4_TOKEN: | |
| print("Pulling evaluation requests and results.") | |
| # try: | |
| # shutil.rmtree("./evals/") | |
| # except: | |
| # pass | |
| repo = Repository( | |
| local_dir="./evals/", | |
| clone_from=LMEH_REPO, | |
| use_auth_token=H4_TOKEN, | |
| repo_type="dataset", | |
| ) | |
| repo.git_pull() | |
| requested_models_dir = "./evals/eval_requests" | |
| requested_models = get_all_requested_models(requested_models_dir) | |
| # parse the results | |
| BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"] | |
| METRICS = ["acc_norm", "acc_norm", "acc_norm", "mc2"] | |
| def load_results(model, benchmark, metric): | |
| file_path = os.path.join("evals", model, f"{model}-eval_{benchmark}.json") | |
| if not os.path.exists(file_path): | |
| return 0.0, None | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| accs = np.array([v[metric] for k, v in data["results"].items()]) | |
| mean_acc = np.mean(accs) | |
| return mean_acc, data["config"]["model_args"] | |
| COLS = [ | |
| "Model", | |
| "Revision", | |
| "Average ⬆️", | |
| "ARC (25-shot) ⬆️", | |
| "HellaSwag (10-shot) ⬆️", | |
| "MMLU (5-shot) ⬆️", | |
| "TruthfulQA (0-shot) ⬆️", | |
| "model_name_for_query", # dummy column to implement search bar (hidden by custom CSS) | |
| ] | |
| TYPES = ["markdown", "str", "number", "number", "number", "number", "number", "str"] | |
| if not IS_PUBLIC: | |
| COLS.insert(2, "8bit") | |
| TYPES.insert(2, "bool") | |
| EVAL_COLS = ["model", "revision", "private", "8bit_eval", "is_delta_weight", "status"] | |
| EVAL_TYPES = ["markdown", "str", "bool", "bool", "bool", "str"] | |
| BENCHMARK_COLS = [ | |
| "ARC (25-shot) ⬆️", | |
| "HellaSwag (10-shot) ⬆️", | |
| "MMLU (5-shot) ⬆️", | |
| "TruthfulQA (0-shot) ⬆️", | |
| ] | |
| 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 repo: | |
| print("Pulling evaluation results for the leaderboard.") | |
| repo.git_pull() | |
| all_data = get_eval_results_dicts(IS_PUBLIC) | |
| if not IS_PUBLIC: | |
| gpt4_values = { | |
| "Model": f'<a target="_blank" href=https://arxiv.org/abs/2303.08774 style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gpt4</a>', | |
| "Revision": "tech report", | |
| "8bit": None, | |
| "Average ⬆️": 84.3, | |
| "ARC (25-shot) ⬆️": 96.3, | |
| "HellaSwag (10-shot) ⬆️": 95.3, | |
| "MMLU (5-shot) ⬆️": 86.4, | |
| "TruthfulQA (0-shot) ⬆️": 59.0, | |
| "model_name_for_query": "GPT-4", | |
| } | |
| all_data.append(gpt4_values) | |
| gpt35_values = { | |
| "Model": f'<a target="_blank" href=https://arxiv.org/abs/2303.08774 style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gpt3.5</a>', | |
| "Revision": "tech report", | |
| "8bit": None, | |
| "Average ⬆️": 71.9, | |
| "ARC (25-shot) ⬆️": 85.2, | |
| "HellaSwag (10-shot) ⬆️": 85.5, | |
| "MMLU (5-shot) ⬆️": 70.0, | |
| "TruthfulQA (0-shot) ⬆️": 47.0, | |
| "model_name_for_query": "GPT-3.5", | |
| } | |
| all_data.append(gpt35_values) | |
| base_line = { | |
| "Model": "<p>Baseline</p>", | |
| "Revision": "N/A", | |
| "8bit": None, | |
| "Average ⬆️": 25.0, | |
| "ARC (25-shot) ⬆️": 25.0, | |
| "HellaSwag (10-shot) ⬆️": 25.0, | |
| "MMLU (5-shot) ⬆️": 25.0, | |
| "TruthfulQA (0-shot) ⬆️": 25.0, | |
| "model_name_for_query": "baseline", | |
| } | |
| all_data.append(base_line) | |
| df = pd.DataFrame.from_records(all_data) | |
| df = df.sort_values(by=["Average ⬆️"], 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 repo: | |
| print("Pulling changes for the evaluation queue.") | |
| repo.git_pull() | |
| entries = [ | |
| entry | |
| for entry in os.listdir("evals/eval_requests") | |
| if not entry.startswith(".") | |
| ] | |
| all_evals = [] | |
| for entry in entries: | |
| if ".json" in entry: | |
| file_path = os.path.join("evals/eval_requests", 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"evals/eval_requests/{entry}") | |
| if not e.startswith(".") | |
| ] | |
| for sub_entry in sub_entries: | |
| file_path = os.path.join("evals/eval_requests", 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] | |
| 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: | |
| config = AutoConfig.from_pretrained(model_name, revision=revision) | |
| return True | |
| except Exception as e: | |
| print("Could not get the model config from the hub.") | |
| print(e) | |
| return False | |
| def add_new_eval( | |
| model: str, | |
| base_model: str, | |
| revision: str, | |
| is_8_bit_eval: bool, | |
| private: bool, | |
| is_delta_weight: bool, | |
| ): | |
| # check the model actually exists before adding the eval | |
| if revision == "": | |
| revision = "main" | |
| if is_delta_weight and not is_model_on_hub(base_model, revision): | |
| error_message = f'Base model "{base_model}" was not found on hub!' | |
| print(error_message) | |
| return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>" | |
| if not is_model_on_hub(model, revision): | |
| error_message = f'Model "{model}"was not found on hub!' | |
| return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>" | |
| 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, | |
| "status": "PENDING", | |
| } | |
| user_name = "" | |
| model_path = model | |
| if "/" in model: | |
| user_name = model.split("/")[0] | |
| model_path = model.split("/")[1] | |
| OUT_DIR = f"eval_requests/{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.lower() in requested_models: | |
| duplicate_request_message = "This model has been already submitted." | |
| return f"<p style='color: orange; font-size: 20px; text-align: center;'>{duplicate_request_message}</p>" | |
| 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", | |
| ) | |
| success_message = "Your request has been submitted to the evaluation queue!" | |
| return f"<p style='color: green; font-size: 20px; text-align: center;'>{success_message}</p>" | |
| 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["model_name_for_query"].str.contains(query, case=False)] | |
| return filtered_df | |
| custom_css = """ | |
| #changelog-text { | |
| font-size: 16px !important; | |
| } | |
| #changelog-text h2 { | |
| font-size: 18px !important; | |
| } | |
| .markdown-text { | |
| font-size: 16px !important; | |
| } | |
| #citation-button span { | |
| font-size: 16px !important; | |
| } | |
| #citation-button textarea { | |
| font-size: 16px !important; | |
| } | |
| #citation-button > label > button { | |
| margin: 6px; | |
| transform: scale(1.3); | |
| } | |
| #leaderboard-table { | |
| margin-top: 15px | |
| } | |
| #search-bar { | |
| padding: 0px; | |
| } | |
| /* Hides the final column */ | |
| table td:last-child, | |
| table th:last-child { | |
| display: none; | |
| } | |
| table td:first-child, | |
| table th:first-child { | |
| max-width: 400px; | |
| overflow: auto; | |
| white-space: nowrap; | |
| } | |
| """ | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.HTML(TITLE) | |
| 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.Box(): | |
| search_bar = gr.Textbox( | |
| placeholder="Search models...", show_label=False, elem_id="search-bar" | |
| ) | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df, | |
| headers=COLS, | |
| datatype=TYPES, | |
| max_rows=5, | |
| 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=5, visible=False | |
| ) | |
| search_bar.change( | |
| search_table, | |
| [hidden_leaderboard_table_for_search, search_bar], | |
| leaderboard_table, | |
| ) | |
| gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
| with gr.Accordion("✅ Finished Evaluations", open=False): | |
| 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): | |
| 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): | |
| pending_eval_table = gr.components.Dataframe( | |
| value=pending_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| max_rows=5, | |
| ) | |
| 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") | |
| base_model_name_textbox = gr.Textbox(label="base model (for delta)") | |
| 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, | |
| ], | |
| submission_result, | |
| ) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=3600) | |
| scheduler.start() | |
| demo.launch() | |