utils.py

import os
import math
import torch
import logging
import subprocess
import numpy as np
import random
import torch.distributed as dist

# from torch._six import inf
from torch import inf
from PIL import Image
from typing import Union, Iterable
from collections import OrderedDict
from torch.utils.tensorboard import SummaryWriter   
from typing import Dict

from diffusers.utils import is_bs4_available, is_ftfy_available

import html
import re
import urllib.parse as ul

if is_bs4_available():
    from bs4 import BeautifulSoup

if is_ftfy_available():
    import ftfy

_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]

#################################################################################
#                             Training Helper Functions                         #
#################################################################################
def fetch_files_by_numbers(start_number, count, file_list):
    file_numbers = range(start_number, start_number + count)
    found_files = []
    for file_number in file_numbers:
        # Specifies the length of the string. The original string is justified to the right and is preceded by a 0 if the number of bits is insufficient
        file_number_padded = str(file_number).zfill(3)
        for file_name in file_list:
            if file_name.endswith(file_number_padded + '.csv'):
                found_files.append(file_name)
                break  # Stop searching once a file is found for the current number
    return found_files

def get_distribute_files(file_list, total_ranks):
    files_per_rank = len(file_list) // total_ranks
    remaining_files = len(file_list) % total_ranks

    distributed_files = []
    start_index = 0

    for rank in range(total_ranks):
        rank_file_count = files_per_rank + 1 if rank < remaining_files else files_per_rank
        meta_files = fetch_files_by_numbers(start_index, rank_file_count, file_list)
        distributed_files.append(meta_files)
        start_index += rank_file_count

    return distributed_files

#################################################################################
#                             Training Clip Gradients                           #
#################################################################################

def get_grad_norm(
        parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor:
    r"""
    Copy from torch.nn.utils.clip_grad_norm_

    Clips gradient norm of an iterable of parameters.

    The norm is computed over all gradients together, as if they were
    concatenated into a single vector. Gradients are modified in-place.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        max_norm (float or int): max norm of the gradients
        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
            infinity norm.
        error_if_nonfinite (bool): if True, an error is thrown if the total
            norm of the gradients from :attr:`parameters` is ``nan``,
            ``inf``, or ``-inf``. Default: False (will switch to True in the future)

    Returns:
        Total norm of the parameter gradients (viewed as a single vector).
    """
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    grads = [p.grad for p in parameters if p.grad is not None]
    norm_type = float(norm_type)
    if len(grads) == 0:
        return torch.tensor(0.)
    device = grads[0].device
    if norm_type == inf:
        norms = [g.detach().abs().max().to(device) for g in grads]
        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
    else:
        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
    return total_norm

def clip_grad_norm_(
        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0,
        error_if_nonfinite: bool = False, clip_grad = True) -> torch.Tensor:
    r"""
    Copy from torch.nn.utils.clip_grad_norm_

    Clips gradient norm of an iterable of parameters.

    The norm is computed over all gradients together, as if they were
    concatenated into a single vector. Gradients are modified in-place.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        max_norm (float or int): max norm of the gradients
        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
            infinity norm.
        error_if_nonfinite (bool): if True, an error is thrown if the total
            norm of the gradients from :attr:`parameters` is ``nan``,
            ``inf``, or ``-inf``. Default: False (will switch to True in the future)

    Returns:
        Total norm of the parameter gradients (viewed as a single vector).
    """
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    grads = [p.grad for p in parameters if p.grad is not None]
    max_norm = float(max_norm)
    norm_type = float(norm_type)
    if len(grads) == 0:
        return torch.tensor(0.)
    device = grads[0].device
    if norm_type == inf:
        norms = [g.detach().abs().max().to(device) for g in grads]
        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
    else:
        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
    # print(total_norm)

    if clip_grad:
        if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
            raise RuntimeError(
                f'The total norm of order {norm_type} for gradients from '
                '`parameters` is non-finite, so it cannot be clipped. To disable '
                'this error and scale the gradients by the non-finite norm anyway, '
                'set `error_if_nonfinite=False`')
        clip_coef = max_norm / (total_norm + 1e-6)
        # Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
        # avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
        # when the gradients do not reside in CPU memory.
        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
        for g in grads:
            g.detach().mul_(clip_coef_clamped.to(g.device))
        # gradient_cliped = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
        # print(gradient_cliped)
    return total_norm

def separation_content_motion(video_clip):
    """
    separate coontent and motion in a given video
    Args:
        video_clip, a give video clip, [B F C H W]

    Return:
        base frame, [B, 1, C, H, W]
        motions, [B, F-1, C, H, W], 
        the first is base frame, 
        the second is motions based on base frame
    """
    total_frames = video_clip.shape[1]
    base_frame = video_clip[0]
    motions = [video_clip[i] - base_frame for i in range(1, total_frames)]
    motions = torch.cat(motions, dim=1)
    return base_frame, motions

def get_experiment_dir(root_dir, args):
    if args.use_compile:
        root_dir += '-Compile' # speedup by torch compile
    if args.fixed_spatial:
        root_dir += '-FixedSpa'
    if args.enable_xformers_memory_efficient_attention:
        root_dir += '-Xfor'
    if args.gradient_checkpointing:
        root_dir += '-Gc'
    if args.mixed_precision:
        root_dir += '-Amp'
    root_dir += f'-{args.image_size[0]}_{args.image_size[1]}'
    return root_dir

#################################################################################
#                             Training Logger                                   #
#################################################################################

def create_logger(logging_dir):
    """
    Create a logger that writes to a log file and stdout.
    """
    if dist.get_rank() == 0:  # real logger
        logging.basicConfig(
            level=logging.INFO,
            # format='[\033[34m%(asctime)s\033[0m] %(message)s',
            format='[%(asctime)s] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
        )
        logger = logging.getLogger(__name__)
        
    else:  # dummy logger (does nothing)
        logger = logging.getLogger(__name__)
        logger.addHandler(logging.NullHandler())
    return logger

def create_accelerate_logger(logging_dir, is_main_process=False):
    """
    Create a logger that writes to a log file and stdout.
    """
    if is_main_process:  # real logger
        logging.basicConfig(
            level=logging.INFO,
            # format='[\033[34m%(asctime)s\033[0m] %(message)s',
            format='[%(asctime)s] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
        )
        logger = logging.getLogger(__name__)
    else:  # dummy logger (does nothing)
        logger = logging.getLogger(__name__)
        logger.addHandler(logging.NullHandler())
    return logger


def create_tensorboard(tensorboard_dir):
    """
    Create a tensorboard that saves losses.
    """
    if dist.get_rank() == 0:  # real tensorboard 
        # tensorboard 
        writer = SummaryWriter(tensorboard_dir)

    return writer

def write_tensorboard(writer, *args):
    '''
    write the loss information to a tensorboard file.
    Only for pytorch DDP mode.
    '''
    if dist.get_rank() == 0:  # real tensorboard
        writer.add_scalar(args[0], args[1], args[2])

#################################################################################
#                      EMA Update/ DDP Training Utils                           #
#################################################################################

def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
    """
    Returns:
        a state dict containing just the attention processor parameters.
    """
    attn_processors = unet.attn_processors

    attn_processors_state_dict = {}

    for attn_processor_key, attn_processor in attn_processors.items():
        for parameter_key, parameter in attn_processor.state_dict().items():
            attn_processors_state_dict[f"{attn_processor_key}.{parameter_key}"] = parameter

    return attn_processors_state_dict

@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
    """
    Step the EMA model towards the current model.
    """
    ema_params = OrderedDict(ema_model.named_parameters())
    model_params = OrderedDict(model.named_parameters())

    for name, param in model_params.items():
        if decay == 0: # initialize
            # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
            ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
        else:
            if param.requires_grad:
                # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
                ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)

# @torch.no_grad()
# def update_ema(ema_model, model, decay=0.9999):
#     for ema_param, model_param in zip(ema_model.parameters(), model.parameters()):
#         if model_param.requires_grad:
#             ema_param.data.mul_(decay).add_(model_param.data, alpha=1 - decay)

def requires_grad(model, flag=True):
    """
    Set requires_grad flag for all parameters in a model.
    """
    for p in model.parameters():
        p.requires_grad = flag

def cleanup():
    """
    End DDP training.
    """
    dist.destroy_process_group()
    

def setup_distributed(backend="nccl", port=None):
    """Initialize distributed training environment.
    support both slurm and torch.distributed.launch
    see torch.distributed.init_process_group() for more details
    """
    num_gpus = torch.cuda.device_count()


    random_integer = random.randint(1, 100)
    print(f'random_integer:{random_integer}')

    if "SLURM_JOB_ID" in os.environ:
        rank = int(os.environ["SLURM_PROCID"])
        world_size = int(os.environ["SLURM_NTASKS"])
        node_list = os.environ["SLURM_NODELIST"]
        addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
        # specify master port
        if port is not None:
            os.environ["MASTER_PORT"] = str(port)
        elif "MASTER_PORT" not in os.environ:
            # os.environ["MASTER_PORT"] = "29566"
            os.environ["MASTER_PORT"] = str(29691 + num_gpus)
            # os.environ["MASTER_PORT"] = str(((random_integer % 101 + 20000)) + num_gpus)
        if "MASTER_ADDR" not in os.environ:
            os.environ["MASTER_ADDR"] = addr
        os.environ["WORLD_SIZE"] = str(world_size)
        os.environ["LOCAL_RANK"] = str(rank % num_gpus)
        os.environ["RANK"] = str(rank)
    else:
        rank = int(os.environ["RANK"])
        world_size = int(os.environ["WORLD_SIZE"])

    # torch.cuda.set_device(rank % num_gpus)

    dist.init_process_group(
        backend=backend,
        world_size=world_size,
        rank=rank,
    )

#################################################################################
#                             Testing  Utils                                    #
#################################################################################

def save_video_grid(video, nrow=None):
    b, t, h, w, c = video.shape
    
    if nrow is None:
        nrow = math.ceil(math.sqrt(b))
    ncol = math.ceil(b / nrow)
    padding = 2
    video_grid = torch.zeros((t, (padding + h) * nrow + padding,
                           (padding + w) * ncol + padding, c), dtype=torch.uint8)
    
    # print(video_grid.shape)
    for i in range(b):
        r = i // ncol
        c = i % ncol
        start_r = (padding + h) * r
        start_c = (padding + w) * c
        video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i]
    
    return video_grid

def save_videos_grid_tav(videos: torch.Tensor, path: str, rescale=False, nrow=None, fps=8):
    from einops import rearrange
    import imageio
    import torchvision

    b, _, _, _, _ = videos.shape
    if nrow is None:
        nrow = math.ceil(math.sqrt(b))
    videos = rearrange(videos, "b c t h w -> t b c h w")
    outputs = []
    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=nrow)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        outputs.append(x)

    # os.makedirs(os.path.dirname(path), exist_ok=True)
    imageio.mimsave(path, outputs, fps=fps)


#################################################################################
#                             MMCV  Utils                                    #
#################################################################################


def collect_env():
    # Copyright (c) OpenMMLab. All rights reserved.
    from mmcv.utils import collect_env as collect_base_env
    from mmcv.utils import get_git_hash
    """Collect the information of the running environments."""
    
    env_info = collect_base_env()
    env_info['MMClassification'] = get_git_hash()[:7]

    for name, val in env_info.items():
        print(f'{name}: {val}')
    
    print(torch.cuda.get_arch_list())
    print(torch.version.cuda)


#################################################################################
#                          Pixart-alpha  Utils                                  #
#################################################################################

bad_punct_regex = re.compile(
        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
)  

def text_preprocessing(text, clean_caption=False):
    if clean_caption and not is_bs4_available():
        clean_caption = False

    if clean_caption and not is_ftfy_available():
        clean_caption = False

    if not isinstance(text, (tuple, list)):
        text = [text]

    def process(text: str):
        if clean_caption:
            text = clean_caption(text)
            text = clean_caption(text)
        else:
            text = text.lower().strip()
        return text

    return [process(t) for t in text]

# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def clean_caption(caption):
    caption = str(caption)
    caption = ul.unquote_plus(caption)
    caption = caption.strip().lower()
    caption = re.sub("<person>", "person", caption)
    # urls:
    caption = re.sub(
        r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
        "",
        caption,
    )  # regex for urls
    caption = re.sub(
        r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
        "",
        caption,
    )  # regex for urls
    # html:
    caption = BeautifulSoup(caption, features="html.parser").text

    # @<nickname>
    caption = re.sub(r"@[\w\d]+\b", "", caption)

    # 31C0—31EF CJK Strokes
    # 31F0—31FF Katakana Phonetic Extensions
    # 3200—32FF Enclosed CJK Letters and Months
    # 3300—33FF CJK Compatibility
    # 3400—4DBF CJK Unified Ideographs Extension A
    # 4DC0—4DFF Yijing Hexagram Symbols
    # 4E00—9FFF CJK Unified Ideographs
    caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
    caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
    caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
    caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
    caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
    caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
    caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
    #######################################################

    # все виды тире / all types of dash --> "-"
    caption = re.sub(
        r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
        "-",
        caption,
    )

    # кавычки к одному стандарту
    caption = re.sub(r"[`´«»“”¨]", '"', caption)
    caption = re.sub(r"[‘’]", "'", caption)

    # &quot;
    caption = re.sub(r"&quot;?", "", caption)
    # &amp
    caption = re.sub(r"&amp", "", caption)

    # ip adresses:
    caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)

    # article ids:
    caption = re.sub(r"\d:\d\d\s+$", "", caption)

    # \n
    caption = re.sub(r"\\n", " ", caption)

    # "#123"
    caption = re.sub(r"#\d{1,3}\b", "", caption)
    # "#12345.."
    caption = re.sub(r"#\d{5,}\b", "", caption)
    # "123456.."
    caption = re.sub(r"\b\d{6,}\b", "", caption)
    # filenames:
    caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)

    #
    caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
    caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""

    caption = re.sub(bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
    caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "

    # this-is-my-cute-cat / this_is_my_cute_cat
    regex2 = re.compile(r"(?:\-|\_)")
    if len(re.findall(regex2, caption)) > 3:
        caption = re.sub(regex2, " ", caption)

    caption = ftfy.fix_text(caption)
    caption = html.unescape(html.unescape(caption))

    caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
    caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
    caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231

    caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
    caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
    caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
    caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
    caption = re.sub(r"\bpage\s+\d+\b", "", caption)

    caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...

    caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)

    caption = re.sub(r"\b\s+\:\s+", r": ", caption)
    caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
    caption = re.sub(r"\s+", " ", caption)

    caption.strip()

    caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
    caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
    caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
    caption = re.sub(r"^\.\S+$", "", caption)

    return caption.strip()