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+1724598809.6558807,1388,1.6007835865020752 diff --git a/sort_result.py b/sort_result.py new file mode 100644 index 00000000..2bccd71c --- /dev/null +++ b/sort_result.py @@ -0,0 +1,146 @@ +import os +import time +import json + +AVG_ONLY = True +GPU_STATUS_FILE = "gpu_status_0824.json" +RESULTS_PATH = r"C:\Users\wengq\Desktop\QY项目\repo\LLaMA-Factory-Mirror\results" +TRAIN_TYPE = "lora_sft" +MODEL_FOLDER = ["Qwen-7B", "Llama2-7B", "ChatGLM2-6B", "Baichuan2-7B"] +MODEL_PREFIX = ["Qwen", "llama2", "ChatGLM2", "Baichuan2"] + +def get_train_result(step500): + all_total_sec = [] + all_step_sec = [] + all_token_per_sec = [] + for model_idx in range(4): + for single in range(2): + total_sec = [] + step_sec = [] + token_per_sec = [] + for run_no in range(1,4): + train_result_path = os.path.join(get_detail_folder_path(MODEL_FOLDER[model_idx], MODEL_PREFIX[model_idx], run_no, single, step500), "train_results.json") + # print(train_result_path) + with open(train_result_path, 'r', encoding='utf-8') as file: + train_results = json.load(file) + # print(train_results) + total_sec.append(train_results['train_runtime']) + step_sec.append(train_results['train_runtime'] / (500 if step500 else 1000)) + token_per_sec.append(train_results['train_tokens_per_second']) + + if AVG_ONLY: + total_sec = [sum(total_sec) / 3] + step_sec = [sum(step_sec) / 3] + token_per_sec = [sum(token_per_sec) / 3] + else: + total_sec.append(sum(total_sec) / 3) + step_sec.append(sum(step_sec) / 3) + token_per_sec.append(sum(token_per_sec) / 3) + + all_total_sec.extend(total_sec) + all_step_sec.extend(step_sec) + all_token_per_sec.extend(token_per_sec) + + result = ",".join(map(str, all_total_sec)) + "\n" + ",".join(map(str, all_step_sec)) + "\n" + ",".join(map(str, all_token_per_sec)) + "\n" + print(result) + return result + +def get_detail_folder_path(model_folder, model_prefix, run_no, single, step500): + detail_folder = model_prefix + "_" + TRAIN_TYPE + "_" + str(run_no) + ("_single" if single else "") + ("_step500" if step500 else "") + return os.path.join(RESULTS_PATH, TRAIN_TYPE, model_folder, detail_folder) + +def get_train_start_end_time(model_folder, model_prefix, run_no, single, step500): + trainer_log_path = os.path.join(get_detail_folder_path(model_folder, model_prefix, run_no, single, step500), "trainer_log.jsonl") + + start_time = time.strptime(get_first_json(trainer_log_path)['cur_time'], "%Y-%m-%d %H:%M:%S") + end_time = time.strptime(get_last_json(trainer_log_path)['cur_time'], "%Y-%m-%d %H:%M:%S") + + return start_time, end_time + + +def get_gpu_result(step500): + all_mem = [] + all_power_consumption = [] + for model_idx in range(4): + for single in range(2): + all_run_max_mem = [] + all_run_avg_power_consumption = [] + for run_no in range(1,4): + start_time, end_time = get_train_start_end_time(MODEL_FOLDER[model_idx], MODEL_PREFIX[model_idx], run_no, single, step500) + max_mems = [] + power_consumptions = [] + gpu_result_path = os.path.join(RESULTS_PATH, "gpu_status", GPU_STATUS_FILE) + with open(gpu_result_path, 'r', encoding='utf-8') as file: + for line in file: + gpu_results = json.loads(line) + cur_time = time.strptime(gpu_results["cur_time"], "%Y-%m-%d %H:%M:%S") + if cur_time >= start_time and cur_time <= end_time: + max_mems.append(get_max_mem_in_multi_gpu(gpu_results)) + power_consumptions.append(get_sum_power_consumption(gpu_results, single)) + + elif cur_time > end_time: + break + all_run_max_mem.append(max(max_mems)) + all_run_avg_power_consumption.append(sum(power_consumptions) / len(power_consumptions)) + + # print(all_run_max_mem) + + if AVG_ONLY: + all_run_max_mem = [sum(all_run_max_mem) / 3] + all_run_avg_power_consumption = [sum(all_run_avg_power_consumption) / 3] + + else: + all_run_max_mem.append(sum(all_run_max_mem) / 3) + all_run_avg_power_consumption.append(sum(all_run_avg_power_consumption) / 3) + + all_mem.extend(all_run_max_mem) + all_power_consumption.extend(all_run_avg_power_consumption) + + + result = ",".join(map(str, all_mem)) + "\n" + ",".join(map(str, all_power_consumption)) + print(result) + return result + +def get_sum_power_consumption(gpu_results, single, card_no = 0): + if single: + return gpu_results["all_gpu_status"][card_no]["powerusage_W"] + else: + sum = 0 + for idx in range(7): + sum += gpu_results["all_gpu_status"][idx]["powerusage_W"] + + return sum + +def get_max_mem_in_multi_gpu(gpu_results): + max_mem = 0 + for gpu in gpu_results["all_gpu_status"]: + if gpu["used_mem_GB"] > max_mem: + max_mem = gpu["used_mem_GB"] + return max_mem + + + +def get_first_json(jsonl_file_path): + first_json = None + with open(jsonl_file_path, 'r', encoding='utf-8') as file: + first_json = json.loads(file.readline()) + return first_json + +def get_last_json(jsonl_file_path): + last_json = None + with open(jsonl_file_path, 'r', encoding='utf-8') as file: + for line in file: + last_json = json.loads(line) + return last_json + + +def main(): + # get_train_result(False) + # get_gpu_result(False) + + get_train_result(True) + get_gpu_result(True) + + +if __name__ == "__main__": + main()