ddp_utils.py

import datetime
import io
import math
import os
import time
from collections import defaultdict, deque

import numpy as np

import torch
import torch.distributed as dist


def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
    """Decay the learning rate"""
    lr = (init_lr - min_lr) * 0.5 * (
        1.0 + math.cos(math.pi * epoch / max_epoch)
    ) + min_lr
    for param_group in optimizer.param_groups:
        param_group["lr"] = lr


def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr):
    """Warmup the learning rate"""
    lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max_step)
    for param_group in optimizer.param_groups:
        param_group["lr"] = lr


def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate):
    """Decay the learning rate"""
    lr = max(min_lr, init_lr * (decay_rate ** epoch))
    for param_group in optimizer.param_groups:
        param_group["lr"] = lr


class SmoothedValue(object):
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """

    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    def synchronize_between_processes(self):
        """
        Warning: does not synchronize the deque!
        """
        if not is_dist_avail_and_initialized():
            return
        t = torch.tensor(
            [self.count, self.total], dtype=torch.float64, device="cuda"
        )
        dist.barrier()
        dist.all_reduce(t)
        t = t.tolist()
        self.count = int(t[0])
        self.total = t[1]

    @property
    def median(self):
        d = torch.tensor(list(self.deque))
        return d.median().item()

    @property
    def avg(self):
        d = torch.tensor(list(self.deque), dtype=torch.float32)
        return d.mean().item()

    @property
    def global_avg(self):
        return self.total / self.count

    @property
    def max(self):
        return max(self.deque)

    @property
    def value(self):
        return self.deque[-1]

    def __str__(self):
        return self.fmt.format(
            median=self.median,
            avg=self.avg,
            global_avg=self.global_avg,
            max=self.max,
            value=self.value,
        )


class MetricLogger(object):
    def __init__(self, delimiter="\t"):
        self.meters = defaultdict(SmoothedValue)
        self.delimiter = delimiter
        self.total_time = 0.0

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.meters[k].update(v)

    def __getattr__(self, attr):
        if attr in self.meters:
            return self.meters[attr]
        if attr in self.__dict__:
            return self.__dict__[attr]
        raise AttributeError(
            "'{}' object has no attribute '{}'".format(
                type(self).__name__, attr
            )
        )

    def __str__(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append("{}: {}".format(name, str(meter)))
        return self.delimiter.join(loss_str)

    def global_avg(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append("{}: {:.4f}".format(name, meter.global_avg))
        return self.delimiter.join(loss_str)

    def synchronize_between_processes(self):
        for meter in self.meters.values():
            meter.synchronize_between_processes()

    def add_meter(self, name, meter):
        self.meters[name] = meter

    def log_every_deit(self, iterable, print_freq, header=None):
        i = 0
        if not header:
            header = ""
        start_time = time.time()
        end = time.time()
        iter_time = SmoothedValue(fmt="{avg:.4f}")
        data_time = SmoothedValue(fmt="{avg:.4f}")
        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
        log_msg = [
            header,
            "[{0" + space_fmt + "}/{1}]",
            "eta: {eta}",
            "{meters}",
            "time: {time}",
            "data: {data}",
        ]
        if torch.cuda.is_available():
            log_msg.append("max mem: {memory:.0f}")
        log_msg = self.delimiter.join(log_msg)
        MB = 1024.0 * 1024.0
        for obj in iterable:
            data_time.update(time.time() - end)
            yield obj
            iter_time.update(time.time() - end)
            if i % print_freq == 0 or i == len(iterable) - 1:
                eta_seconds = iter_time.global_avg * (len(iterable) - i)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                if torch.cuda.is_available():
                    print(
                        log_msg.format(
                            i,
                            len(iterable),
                            eta=eta_string,
                            meters=str(self),
                            time=str(iter_time),
                            data=str(data_time),
                            memory=torch.cuda.max_memory_allocated() / MB,
                        )
                    )
                else:
                    print(
                        log_msg.format(
                            i,
                            len(iterable),
                            eta=eta_string,
                            meters=str(self),
                            time=str(iter_time),
                            data=str(data_time),
                        )
                    )
            i += 1
            end = time.time()
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print(
            "{} Total time: {} ({:.4f} s / it)".format(
                header, total_time_str, total_time / len(iterable)
            )
        )

    def log_every(
        self, cur_iter, total_iters, batch_time, print_freq, header=None
    ):
        if not header:
            header = ""

        self.total_time += batch_time
        space_fmt = ":" + str(total_iters) + "d"
        log_msg = [
            header,
            "[{0}/{1}]",
            "eta: {eta}",
            "{meters}",
        ]
        if torch.cuda.is_available():
            log_msg.append("max mem: {memory:.0f}")
        log_msg = self.delimiter.join(log_msg)
        MB = 1024.0 * 1024.0

        if cur_iter % print_freq == 0 or cur_iter == (total_iters - 1):
            eta_seconds = batch_time * (total_iters - cur_iter)
            eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
            if torch.cuda.is_available():
                print(
                    log_msg.format(
                        cur_iter,
                        total_iters,
                        eta=eta_string,
                        meters=str(self),
                        memory=torch.cuda.max_memory_allocated() / MB,
                    )
                )
            else:
                print(
                    log_msg.format(
                        cur_iter,
                        total_iters,
                        eta=eta_string,
                        meters=str(self),
                    )
                )

        if cur_iter == total_iters - 1:
            total_time = self.total_time
            total_time_str = str(datetime.timedelta(seconds=int(total_time)))
            print(
                "{} Total time: {} ({:.4f} s / it)".format(
                    header, total_time_str, total_time / total_iters
                )
            )


class AttrDict(dict):
    def __init__(self, *args, **kwargs):
        super(AttrDict, self).__init__(*args, **kwargs)
        self.__dict__ = self


def compute_acc(logits, label, reduction="mean"):
    ret = (torch.argmax(logits, dim=1) == label).float()
    if reduction == "none":
        return ret.detach()
    elif reduction == "mean":
        return ret.mean().item()


def compute_n_params(model, return_str=True):
    tot = 0
    for p in model.parameters():
        w = 1
        for x in p.shape:
            w *= x
        tot += w
    if return_str:
        if tot >= 1e6:
            return "{:.1f}M".format(tot / 1e6)
        else:
            return "{:.1f}K".format(tot / 1e3)
    else:
        return tot


def setup_for_distributed(is_master):
    """
    This function disables printing when not in master process
    """
    import builtins as __builtin__

    builtin_print = __builtin__.print

    def print(*args, **kwargs):
        force = kwargs.pop("force", False)
        if is_master or force:
            builtin_print(*args, **kwargs)

    __builtin__.print = print


def is_dist_avail_and_initialized():
    if not dist.is_available():
        return False
    if not dist.is_initialized():
        return False
    return True


def get_world_size():
    if not is_dist_avail_and_initialized():
        return 1
    return dist.get_world_size()


def get_rank():
    if not is_dist_avail_and_initialized():
        return 0
    return dist.get_rank()


def is_main_process():
    return get_rank() == 0


def save_on_master(*args, **kwargs):
    if is_main_process():
        torch.save(*args, **kwargs)


def init_distributed_mode(args):
    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
        args.rank = int(os.environ["RANK"])
        args.world_size = int(os.environ["WORLD_SIZE"])
        args.gpu = int(os.environ["LOCAL_RANK"])
    elif "SLURM_PROCID" in os.environ:
        args.rank = int(os.environ["SLURM_PROCID"])
        args.gpu = args.rank % torch.cuda.device_count()
    else:
        print("Not using distributed mode")
        args.distributed = False
        return

    args.distributed = True

    torch.cuda.set_device(args.gpu)
    args.dist_backend = "nccl"
    print(
        "| distributed init (rank {}, word {}): {}".format(
            args.rank, args.world_size, args.dist_url
        ),
        flush=True,
    )

    torch.distributed.init_process_group(
        backend=args.dist_backend,
        init_method=args.dist_url,
        timeout=datetime.timedelta(seconds=5400),
        world_size=args.world_size,
        rank=args.rank,
    )
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)


def get_last_checkpoint(output_dir):
    """
    Get the last checkpoint from the checkpointing folder.
    Args:
        path_to_job (string): the path to the folder of the current job.
    """

    files = os.listdir(output_dir)
    names = [f for f in files if "checkpoint" in f]
    if len(names) == 0:
        return None

    else:
        name = sorted(names)[-1]
        return os.path.join(output_dir, name)


def build_optimizer(model, config):
    """
    Build optimizer, set weight decay of normalization to 0 by default.
    """
    optimizer = torch.optim.AdamW(
        model.parameters(),
        lr=config["init_lr"],
        weight_decay=config["weight_decay"],
    )

    return optimizer


def set_weight_decay(model, skip_list=(), skip_keywords=()):
    has_decay = []
    no_decay = []

    for name, param in model.named_parameters():
        if not param.requires_grad:
            continue  # frozen weights
        if (
            len(param.shape) == 1
            or name.endswith(".bias")
            or (name in skip_list)
            or check_keywords_in_name(name, skip_keywords)
        ):
            no_decay.append(param)
            # print(f"{name} has no weight decay")
        else:
            has_decay.append(param)

    return [{"params": has_decay}, {"params": no_decay, "weight_decay": 0.0}]


def check_keywords_in_name(name, keywords=()):
    isin = False
    for keyword in keywords:
        if keyword in name:
            isin = True
    return isin


def _load_checkpoint_for_ema(model_ema, checkpoint):
    """
    Workaround for ModelEma._load_checkpoint to accept an already-loaded object
    """
    mem_file = io.BytesIO()
    torch.save(checkpoint, mem_file)
    mem_file.seek(0)
    model_ema._load_checkpoint(mem_file)