import json import os from tqdm import tqdm import copy import pandas as pd import numpy as np from src.display.formatting import has_no_nan_values, make_clickable_model from src.display.utils import AutoEvalColumn, EvalQueueColumn from src.leaderboard.filter_models import filter_models from src.leaderboard.read_evals import get_raw_eval_results, EvalResult, update_model_type_with_open_llm_request_file from src.backend.envs import Tasks as BackendTasks from src.display.utils import Tasks from src.display.utils import E2Es, PREs, TS def get_leaderboard_df( results_path: str, requests_path: str, requests_path_open_llm: str, cols: list, benchmark_cols: list, is_backend: bool = False, ) -> tuple[list[EvalResult], pd.DataFrame]: # Returns a list of EvalResult raw_data: list[EvalResult] = get_raw_eval_results(results_path, requests_path, requests_path_open_llm) if requests_path_open_llm != "": for result_idx in tqdm(range(len(raw_data)), desc="updating model type with open llm leaderboard"): raw_data[result_idx] = update_model_type_with_open_llm_request_file( raw_data[result_idx], requests_path_open_llm ) # all_data_json_ = [v.to_dict() for v in raw_data if v.is_complete()] all_data_json_ = [v.to_dict() for v in raw_data] # include incomplete evals name_to_bm_map = {} task_iterator = Tasks if is_backend is True: task_iterator = BackendTasks for task in task_iterator: task = task.value name = task.col_name bm = (task.benchmark, task.metric) name_to_bm_map[name] = bm # bm_to_name_map = {bm: name for name, bm in name_to_bm_map.items()} system_metrics_to_name_map = { "end_to_end_time": f"{E2Es}", "prefilling_time": f"{PREs}", "decoding_throughput": f"{TS}", } all_data_json = [] for entry in all_data_json_: new_entry = copy.deepcopy(entry) for k, v in entry.items(): if k in name_to_bm_map: benchmark, metric = name_to_bm_map[k] new_entry[k] = entry[k][metric] for sys_metric, metric_namne in system_metrics_to_name_map.items(): if sys_metric in entry[k]: new_entry[f"{k} {metric_namne}"] = entry[k][sys_metric] all_data_json += [new_entry] # all_data_json.append(baseline_row) filter_models(all_data_json) df = pd.DataFrame.from_records(all_data_json) # if AutoEvalColumn.average.name in df: # df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) for col in cols: if col not in df.columns: df[col] = np.nan if not df.empty: df = df.round(decimals=2) # filter out if any of the benchmarks have not been produced # df = df[has_no_nan_values(df, benchmark_cols)] return raw_data, df def get_evaluation_queue_df(save_path: str, cols: list) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")] all_evals = [] for entry in entries: if ".json" in entry: file_path = os.path.join(save_path, entry) with open(file_path) as fp: data = json.load(fp) data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") data[EvalQueueColumn.model_framework.name] = data.get("inference_framework", "-") all_evals.append(data) elif ".md" not in entry: # this is a folder sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")] for sub_entry in sub_entries: file_path = os.path.join(save_path, entry, sub_entry) with open(file_path) as fp: data = json.load(fp) data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") data[EvalQueueColumn.model_framework.name] = data.get("inference_framework", "-") 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") or e["status"] == "PENDING_NEW_EVAL"] df_pending = pd.DataFrame.from_records(pending_list, columns=cols) df_running = pd.DataFrame.from_records(running_list, columns=cols) df_finished = pd.DataFrame.from_records(finished_list, columns=cols) return df_finished[cols], df_running[cols], df_pending[cols]