utils.py

import contextlib
import os
import gc
import random
import shutil
import time
import traceback
import zipfile

import filelock
import numpy as np
import pandas as pd
import torch


def set_seed(seed: int):
    """
    Sets the seed of the entire notebook so results are the same every time we run.
    This is for REPRODUCIBILITY.
    """
    np.random.seed(seed)
    random_state = np.random.RandomState(seed)
    random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    os.environ['PYTHONHASHSEED'] = str(seed)
    return random_state


def flatten_list(lis):
    """Given a list, possibly nested to any level, return it flattened."""
    new_lis = []
    for item in lis:
        if type(item) == type([]):
            new_lis.extend(flatten_list(item))
        else:
            new_lis.append(item)
    return new_lis


def clear_torch_cache():
    if torch.cuda.is_available:
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()
        gc.collect()


def system_info():
    import psutil

    system = {}
    # https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard
    # https://arshren.medium.com/monitoring-your-devices-in-python-5191d672f749
    temps = psutil.sensors_temperatures(fahrenheit=False)
    if 'coretemp' in temps:
        coretemp = temps['coretemp']
        temp_dict = {k.label: k.current for k in coretemp}
        for k, v in temp_dict.items():
            system['CPU_C/%s' % k] = v

    # https://github.com/gpuopenanalytics/pynvml/blob/master/help_query_gpu.txt
    from pynvml.smi import nvidia_smi
    nvsmi = nvidia_smi.getInstance()

    gpu_power_dict = {'W_gpu%d' % i: x['power_readings']['power_draw'] for i, x in
                      enumerate(nvsmi.DeviceQuery('power.draw')['gpu'])}
    for k, v in gpu_power_dict.items():
        system['GPU_W/%s' % k] = v

    gpu_temp_dict = {'C_gpu%d' % i: x['temperature']['gpu_temp'] for i, x in
                     enumerate(nvsmi.DeviceQuery('temperature.gpu')['gpu'])}
    for k, v in gpu_temp_dict.items():
        system['GPU_C/%s' % k] = v

    gpu_memory_free_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['free'] for i, x in
                            enumerate(nvsmi.DeviceQuery('memory.free')['gpu'])}
    gpu_memory_total_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['total'] for i, x in
                             enumerate(nvsmi.DeviceQuery('memory.total')['gpu'])}
    gpu_memory_frac_dict = {k: gpu_memory_free_dict[k] / gpu_memory_total_dict[k] for k in gpu_memory_total_dict}
    for k, v in gpu_memory_frac_dict.items():
        system[f'GPU_M/%s' % k] = v

    return system


def system_info_print():
    try:
        df = pd.DataFrame.from_dict(system_info(), orient='index')
        # avoid slamming GPUs
        time.sleep(1)
        return df.to_markdown()
    except Exception as e:
        return "Error: %s" % str(e)


def zip_data(root_dirs=None, zip_path='data.zip', base_dir='./'):
    try:
        return _zip_data(zip_path=zip_path, base_dir=base_dir, root_dirs=root_dirs)
    except Exception as e:
        traceback.print_exc()
        print('Exception in zipping: %s' % str(e))


def _zip_data(root_dirs=None, zip_path='data.zip', base_dir='./'):
    assert root_dirs is not None
    with zipfile.ZipFile(zip_path, "w") as expt_zip:
        for root_dir in root_dirs:
            if root_dir is None:
                continue
            for root, d, files in os.walk(root_dir):
                for file in files:
                    file_to_archive = os.path.join(root, file)
                    assert os.path.exists(file_to_archive)
                    path_to_archive = os.path.relpath(file_to_archive, base_dir)
                    expt_zip.write(filename=file_to_archive, arcname=path_to_archive)
    return "data.zip"


def save_generate_output(output=None, base_model=None, json_file_path=None):
    try:
        return _save_generate_output(output=output, base_model=base_model, json_file_path=json_file_path)
    except Exception as e:
        traceback.print_exc()
        print('Exception in saving: %s' % str(e))


def _save_generate_output(output=None, base_model=None, json_file_path=None):
    """
    Save conversation to .json, row by row
    Appends if file exists
    """
    assert isinstance(json_file_path, str), "must provide save_path"
    as_file = os.path.normpath(json_file_path)
    if os.path.isfile(as_file):
        # protection if had file there before
        os.remove(as_file)
    os.makedirs(json_file_path, exist_ok=True)
    json_file_file = os.path.join(json_file_path, 'save.json')
    import json
    if output[-10:] == '\n\n<human>:':
        # remove trailing <human>:
        output = output[:-10]
    with filelock.FileLock("save_path.lock"):
        # lock logging in case have concurrency
        with open(json_file_file, "a") as f:
            # just add [ at start, and ] at end, and have proper JSON dataset
            f.write(
                "  " + json.dumps(
                    dict(text=output, time=time.ctime(), base_model=base_model)
                ) + ",\n"
            )