import fire import time import json from collections import defaultdict from .basic_stats import get_log_files, NUM_SERVERS, LOG_ROOT_DIR from .utils import detect_language, get_time_stamp_from_date, get_input_image_path, load_image_from_path from tqdm import tqdm VOTES = ["tievote", "leftvote", "rightvote", "bothbad_vote", "chat"] def remove_html(raw): if raw.startswith("

"): return raw[raw.find(": ") + 2 : -len("

\n")] if raw.startswith("### Model A: ") or raw.startswith("### Model B: "): return raw[13:] return raw def read_file(filename): data = [] for retry in range(5): try: # lines = open(filename).readlines() for l in open(filename): row = json.loads(l) if row["type"] in VOTES: data.append(row) break except FileNotFoundError: time.sleep(2) return data def read_file_parallel(log_files, num_threads=16): data_all = [] from multiprocessing import Pool with Pool(num_threads) as p: ret_all = list(tqdm(p.imap(read_file, log_files), total=len(log_files))) for ret in ret_all: data_all.extend(ret) return data_all def num_tokens(s:str): if s is None: return 0 return len(s) / 4 def main( ): log_files = get_log_files() data = read_file_parallel(log_files) all_model_counts = defaultdict(int) all_model_input_tokens_counts = defaultdict(list) all_model_output_tokens_counts = defaultdict(list) all_model_image_sizes = defaultdict(list) chat_battle_counts = defaultdict(int) for row in tqdm(data, desc="counting"): if row['type'] == "chat": chat_battle_counts["chat"] += 1 all_model_counts[row['model']] += 1 tstamp = row["tstamp"] conv_id = row["state"]["conv_id"] image = load_image_from_path(get_input_image_path(tstamp, conv_id)) if image is None: image_size = None else: image_size = load_image_from_path(get_input_image_path(tstamp, conv_id)).size all_model_image_sizes[row['model']].append(image_size) try: for message in row["state"]["messages"][row["state"]["offset"] :: 2]: all_model_input_tokens_counts[row['model']].append(num_tokens(message[1])) for message in row["state"]["messages"][row["state"]["offset"] + 1 :: 2]: all_model_output_tokens_counts[row['model']].append(num_tokens(message[1])) except Exception as e: print(row) raise e else: chat_battle_counts[row['type']] += 1 if row["models"][0] is None or row["models"][1] is None: continue # Resolve model names models_public = [remove_html(row["models"][0]), remove_html(row["models"][1])] if "model_name" in row["states"][0]: models_hidden = [ row["states"][0]["model_name"], row["states"][1]["model_name"], ] if models_hidden[0] is None: models_hidden = models_public else: models_hidden = models_public if (models_public[0] == "" and models_public[1] != "") or ( models_public[1] == "" and models_public[0] != "" ): continue if models_public[0] == "" or models_public[0] == "Model A": anony = True models = models_hidden else: anony = False models = models_public if not models_public == models_hidden: continue all_model_counts[models[0]] += 1 all_model_counts[models[1]] += 1 tstamp = row["tstamp"] conv_id1 = row["states"][0]["conv_id"] conv_id2 = row["states"][1]["conv_id"] image1 = load_image_from_path(get_input_image_path(tstamp, conv_id1)) image2 = load_image_from_path(get_input_image_path(tstamp, conv_id2)) all_model_image_sizes[models[0]].append(None if image1 is None else image1.size) all_model_image_sizes[models[1]].append(None if image2 is None else image2.size) for message in row["states"][0]["messages"][row["states"][0]["offset"] :: 2]: all_model_input_tokens_counts[models[0]].append(num_tokens(message[1])) for message in row["states"][0]["messages"][row["states"][0]["offset"] + 1 :: 2]: all_model_output_tokens_counts[models[0]].append(num_tokens(message[1])) for message in row["states"][1]["messages"][row["states"][1]["offset"] :: 2]: all_model_input_tokens_counts[models[1]].append(num_tokens(message[1])) for message in row["states"][1]["messages"][row["states"][1]["offset"] + 1 :: 2]: all_model_output_tokens_counts[models[1]].append(num_tokens(message[1])) print("### Chat battle counts (requests)") print(json.dumps(chat_battle_counts, indent=4)) print("### Model counts (requests)") print(json.dumps(all_model_counts, indent=4)) print("### Model Avg input tokens counts (tokens)") average_input_tokens_counts = {} for model, counts in all_model_input_tokens_counts.items(): average_input_tokens_counts[model] = sum(counts) / len(counts) print(json.dumps(average_input_tokens_counts, indent=4)) print("### Model AVg output tokens counts (tokens)") average_output_tokens_counts = {} for model, counts in all_model_output_tokens_counts.items(): average_output_tokens_counts[model] = sum(counts) / len(counts) print(json.dumps(average_output_tokens_counts, indent=4)) print("### Model Avg image sizes (height, width)") average_image_sizes = {} for model, sizes in all_model_image_sizes.items(): avg_height = sum([size[0] for size in sizes if size is not None]) / len(sizes) avg_width = sum([size[1] for size in sizes if size is not None]) / len(sizes) average_image_sizes[model] = (avg_height, avg_width) print(json.dumps(average_image_sizes, indent=4)) print("### GPT-4V estimated cost (USD)") gpt_4v_name = "gpt-4-vision-preview" gpt_4v_cost = {} gpt_4v_cost['input'] = sum(all_model_input_tokens_counts[gpt_4v_name]) / 1000 * 0.01 gpt_4v_cost['output'] = sum(all_model_output_tokens_counts[gpt_4v_name]) / 1000 * 0.03 all_image_cost = 0 for size in all_model_image_sizes[gpt_4v_name]: if size is None: continue all_image_tokens = (size[0] // 512 + 1) * (size[1] // 512 + 1) * 170 + 85 all_image_cost += all_image_tokens / 1000 * 0.01 gpt_4v_cost['image'] = all_image_cost print(json.dumps(gpt_4v_cost, indent=4)) if __name__ == "__main__": fire.Fire(main)