trainer_misc/utils.py

import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque, OrderedDict
import datetime
import numpy as np


from pathlib import Path
import argparse

import torch
from torch import optim as optim
import torch.distributed as dist

try:
    from torch._six import inf
except ImportError:
    from torch import inf

from tensorboardX import SummaryWriter


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 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 init_distributed_mode(args, init_pytorch_ddp=True):
    if int(os.getenv('OMPI_COMM_WORLD_SIZE', '0')) > 0:
        rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
        local_rank = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
        world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])

        os.environ["LOCAL_RANK"] = os.environ['OMPI_COMM_WORLD_LOCAL_RANK']
        os.environ["RANK"] = os.environ['OMPI_COMM_WORLD_RANK']
        os.environ["WORLD_SIZE"] = os.environ['OMPI_COMM_WORLD_SIZE']

        args.rank = int(os.environ["RANK"])
        args.world_size = int(os.environ["WORLD_SIZE"])
        args.gpu = int(os.environ["LOCAL_RANK"])

    elif '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'])

    else:
        print('Not using distributed mode')
        args.distributed = False
        return

    args.distributed = True
    args.dist_backend = 'nccl'
    args.dist_url = "env://"
    print('| distributed init (rank {}): {}, gpu {}'.format(
        args.rank, args.dist_url, args.gpu), flush=True)

    if init_pytorch_ddp:
        # Init DDP Group, for script without using accelerate framework
        torch.cuda.set_device(args.gpu)
        torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                world_size=args.world_size, rank=args.rank, timeout=datetime.timedelta(days=365))
        torch.distributed.barrier()
        setup_for_distributed(args.rank == 0)


def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, 

        start_warmup_value=0, warmup_steps=-1):
    warmup_schedule = np.array([])
    warmup_iters = warmup_epochs * niter_per_ep
    if warmup_steps > 0:
        warmup_iters = warmup_steps
    print("Set warmup steps = %d" % warmup_iters)
    if warmup_epochs > 0:
        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)

    iters = np.arange(epochs * niter_per_ep - warmup_iters)
    schedule = np.array(
        [final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])

    schedule = np.concatenate((warmup_schedule, schedule))

    assert len(schedule) == epochs * niter_per_ep
    return schedule


def constant_scheduler(base_value, epochs, niter_per_ep, warmup_epochs=0, 

        start_warmup_value=1e-6, warmup_steps=-1):
    warmup_schedule = np.array([])
    warmup_iters = warmup_epochs * niter_per_ep
    if warmup_steps > 0:
        warmup_iters = warmup_steps
    print("Set warmup steps = %d" % warmup_iters)
    if warmup_iters > 0:
        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)

    iters = epochs * niter_per_ep - warmup_iters
    schedule = np.array([base_value] * iters)
    
    schedule = np.concatenate((warmup_schedule, schedule))

    assert len(schedule) == epochs * niter_per_ep
    return schedule


def get_parameter_groups(model, weight_decay=1e-5, base_lr=1e-4, skip_list=(), get_num_layer=None, get_layer_scale=None, **kwargs):
    parameter_group_names = {}
    parameter_group_vars = {}

    for name, param in model.named_parameters():
        if not param.requires_grad:
            continue  # frozen weights
        if len(kwargs.get('filter_name', [])) > 0:
            flag = False
            for filter_n in kwargs.get('filter_name', []):
                if filter_n in name:
                    print(f"filter {name} because of the pattern {filter_n}")
                    flag = True
            if flag:
                continue

        default_scale=1.
        
        if param.ndim <= 1 or name.endswith(".bias") or name in skip_list: # param.ndim <= 1 len(param.shape) == 1
            group_name = "no_decay"
            this_weight_decay = 0.
        else:
            group_name = "decay"
            this_weight_decay = weight_decay

        if get_num_layer is not None:
            layer_id = get_num_layer(name)
            group_name = "layer_%d_%s" % (layer_id, group_name)
        else:
            layer_id = None

        if group_name not in parameter_group_names:
            if get_layer_scale is not None:
                scale = get_layer_scale(layer_id)
            else:
                scale = default_scale

            parameter_group_names[group_name] = {
                "weight_decay": this_weight_decay,
                "params": [],
                "lr": base_lr,
                "lr_scale": scale,
            }

            parameter_group_vars[group_name] = {
                "weight_decay": this_weight_decay,
                "params": [],
                "lr": base_lr,
                "lr_scale": scale,
            }

        parameter_group_vars[group_name]["params"].append(param)
        parameter_group_names[group_name]["params"].append(name)

    print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
    return list(parameter_group_vars.values())


def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None, **kwargs):
    opt_lower = args.opt.lower()
    weight_decay = args.weight_decay

    skip = {}
    if skip_list is not None:
        skip = skip_list
    elif hasattr(model, 'no_weight_decay'):
        skip = model.no_weight_decay()
    print(f"Skip weight decay name marked in model: {skip}")
    parameters = get_parameter_groups(model, weight_decay, args.lr, skip, get_num_layer, get_layer_scale, **kwargs)
    weight_decay = 0.

    if 'fused' in opt_lower:
        assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'

    opt_args = dict(lr=args.lr, weight_decay=weight_decay)
    if hasattr(args, 'opt_eps') and args.opt_eps is not None:
        opt_args['eps'] = args.opt_eps
    if hasattr(args, 'opt_beta1') and args.opt_beta1 is not None:
        opt_args['betas'] = (args.opt_beta1, args.opt_beta2)
    
    print('Optimizer config:', opt_args)
    opt_split = opt_lower.split('_')
    opt_lower = opt_split[-1]
    if opt_lower == 'sgd' or opt_lower == 'nesterov':
        opt_args.pop('eps', None)
        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
    elif opt_lower == 'momentum':
        opt_args.pop('eps', None)
        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
    elif opt_lower == 'adam':
        optimizer = optim.Adam(parameters, **opt_args)
    elif opt_lower == 'adamw':
        optimizer = optim.AdamW(parameters, **opt_args)
    elif opt_lower == 'adadelta':
        optimizer = optim.Adadelta(parameters, **opt_args)
    elif opt_lower == 'rmsprop':
        optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
    else:
        assert False and "Invalid optimizer"
        raise ValueError

    return optimizer


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

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            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 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(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 auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None):
    output_dir = Path(args.output_dir)
    if args.auto_resume and len(args.resume) == 0:
        all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint.pth'))
        if len(all_checkpoints) > 0:
            args.resume = os.path.join(output_dir, 'checkpoint.pth')
        else:
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
            latest_ckpt = -1
            for ckpt in all_checkpoints:
                t = ckpt.split('-')[-1].split('.')[0]
                if t.isdigit():
                    latest_ckpt = max(int(t), latest_ckpt)
            if latest_ckpt >= 0:
                args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
        print("Auto resume checkpoint: %s" % args.resume)

    if args.resume:
        if args.resume.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.resume, map_location='cpu', check_hash=True)
        else:
            checkpoint = torch.load(args.resume, map_location='cpu')
    
        model_without_ddp.load_state_dict(checkpoint['model']) # strict: bool=True, , strict=False
        print("Resume checkpoint %s" % args.resume)

        if ('optimizer' in checkpoint) and ('epoch' in checkpoint) and (optimizer is not None):
            optimizer.load_state_dict(checkpoint['optimizer'])
            print(f"Resume checkpoint at epoch {checkpoint['epoch']}, the global optmization step is {checkpoint['step']}")
            args.start_epoch = checkpoint['epoch'] + 1
            args.global_step = checkpoint['step'] + 1
            if model_ema is not None:
                if 'model_ema' in checkpoint:
                    ema_load_res = model_ema.load_state_dict(checkpoint["model_ema"])
                print(f"EMA Model Resume results: {ema_load_res}")
            if 'scaler' in checkpoint:
                loss_scaler.load_state_dict(checkpoint['scaler'])
            print("With optim & sched!")
        if ('optimizer_disc' in checkpoint) and (optimizer_disc is not None):
            optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])


def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None, save_ckpt_freq=1):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)

    checkpoint_paths = [output_dir / 'checkpoint.pth']
    if epoch == 'best':
        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name),]
    elif (epoch + 1) % save_ckpt_freq == 0:
        checkpoint_paths.append(output_dir / ('checkpoint-%s.pth' % epoch_name))

    for checkpoint_path in checkpoint_paths:
        to_save = {
            'model': model_without_ddp.state_dict(),
            'epoch': epoch,
            'step' : args.global_step,
            'args': args,
        }

        if optimizer is not None:
            to_save['optimizer'] = optimizer.state_dict()

        if loss_scaler is not None:
            to_save['scaler'] = loss_scaler.state_dict()

        if model_ema is not None:
            to_save['model_ema'] = model_ema.state_dict()
            
        if optimizer_disc is not None:
            to_save['optimizer_disc'] = optimizer_disc.state_dict()

        save_on_master(to_save, checkpoint_path)


def get_grad_norm_(parameters, norm_type: float = 2.0, layer_names=None) -> torch.Tensor:
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    
    parameters = [p for p in parameters if p.grad is not None]
        
    norm_type = float(norm_type)
    if len(parameters) == 0:
        return torch.tensor(0.)
    device = parameters[0].grad.device
    
    if norm_type == inf:
        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
    else:
        layer_norm = torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters])
        total_norm = torch.norm(layer_norm, norm_type)
        
        if layer_names is not None:
            if torch.isnan(total_norm) or torch.isinf(total_norm) or total_norm > 1.0:
                value_top, name_top = torch.topk(layer_norm, k=5)
                print(f"Top norm value: {value_top}")
                print(f"Top norm name: {[layer_names[i][7:] for i in name_top.tolist()]}")
        
    return total_norm


class NativeScalerWithGradNormCount:
    state_dict_key = "amp_scaler"

    def __init__(self, enabled=True):
        print(f"Set the loss scaled to {enabled}")
        self._scaler = torch.cuda.amp.GradScaler(enabled=enabled)

    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True, layer_names=None):
        self._scaler.scale(loss).backward(create_graph=create_graph)
        if update_grad:
            if clip_grad is not None:
                assert parameters is not None
                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
            else:
                self._scaler.unscale_(optimizer)
                norm = get_grad_norm_(parameters, layer_names=layer_names)
            self._scaler.step(optimizer)
            self._scaler.update()
        else:
            norm = None
        return norm

    def state_dict(self):
        return self._scaler.state_dict()

    def load_state_dict(self, state_dict): 
        self._scaler.load_state_dict(state_dict)