utils.py

# --------------------------------------------------------
# Original Code from BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict

from pathlib import Path

import torch
import torch.distributed as dist
from torch import nn
from torch._six import inf

from tensorboardX import SummaryWriter


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)))


class TensorboardLogger(object):
    def __init__(self, log_dir):
        self.writer = SummaryWriter(logdir=log_dir)
        self.step = 0

    def set_step(self, step=None):
        if step is not None:
            self.step = step
        else:
            self.step += 1

    def update(self, head='scalar', step=None, **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.writer.add_scalar(head + "/" + k, v, self.step if step is None else step)

    def flush(self):
        self.writer.flush()


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)


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 args.dist_on_itp:
        args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
        args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
        args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
        args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
        os.environ['LOCAL_RANK'] = str(args.gpu)
        os.environ['RANK'] = str(args.rank)
        os.environ['WORLD_SIZE'] = str(args.world_size)
        # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "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'])
    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 {}): {}, gpu {}'.format(
        args.rank, args.dist_url, args.gpu), flush=True)
    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                         world_size=args.world_size, rank=args.rank)
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)


def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
    missing_keys = []
    unexpected_keys = []
    error_msgs = []
    # copy state_dict so _load_from_state_dict can modify it
    metadata = getattr(state_dict, '_metadata', None)
    state_dict = state_dict.copy()
    if metadata is not None:
        state_dict._metadata = metadata

    def load(module, prefix=''):
        local_metadata = {} if metadata is None else metadata.get(
            prefix[:-1], {})
        module._load_from_state_dict(
            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + '.')

    load(model, prefix=prefix)

    warn_missing_keys = []
    ignore_missing_keys = []
    for key in missing_keys:
        keep_flag = True
        for ignore_key in ignore_missing.split('|'):
            if ignore_key in key:
                keep_flag = False
                break
        if keep_flag:
            warn_missing_keys.append(key)
        else:
            ignore_missing_keys.append(key)

    missing_keys = warn_missing_keys

    if len(missing_keys) > 0:
        print("Weights of {} not initialized from pretrained model: {}".format(
            model.__class__.__name__, missing_keys))
    if len(unexpected_keys) > 0:
        print("Weights from pretrained model not used in {}: {}".format(
            model.__class__.__name__, unexpected_keys))
    if len(ignore_missing_keys) > 0:
        print("Ignored weights of {} not initialized from pretrained model: {}".format(
            model.__class__.__name__, ignore_missing_keys))
    if len(error_msgs) > 0:
        print('\n'.join(error_msgs))


class NativeScalerWithGradNormCount:
    state_dict_key = "amp_scaler"

    def __init__(self):
        self._scaler = torch.cuda.amp.GradScaler()

    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
        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)
            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)


def get_grad_norm_(parameters, norm_type: float = 2.0) -> 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:
        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
    return total_norm


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 accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)
    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.reshape(1, -1).expand_as(pred))
    return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]


def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)
    if loss_scaler is not None:
        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
        for checkpoint_path in checkpoint_paths:
            to_save = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'epoch': epoch,
                'scaler': loss_scaler.state_dict(),
                'args': args,
            }

            if model_ema is not None:
                to_save['model_ema'] = get_state_dict(model_ema)

            save_on_master(to_save, checkpoint_path)
    else:
        client_state = {'epoch': epoch}
        if model_ema is not None:
            client_state['model_ema'] = get_state_dict(model_ema)
        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)


def save_model_copy(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)
    if loss_scaler is not None:
        checkpoint_paths = [output_dir / ('checkpoint_copy-%s.pth' % epoch_name)]
        for checkpoint_path in checkpoint_paths:
            to_save = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'epoch': epoch,
                'scaler': loss_scaler.state_dict(),
                'args': args,
            }

            if model_ema is not None:
                to_save['model_ema'] = get_state_dict(model_ema)

            save_on_master(to_save, checkpoint_path)
    else:
        client_state = {'epoch': epoch}
        if model_ema is not None:
            client_state['model_ema'] = get_state_dict(model_ema)
        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint_copy-%s" % epoch_name, client_state=client_state)

def save_decoder(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)
    if loss_scaler is not None:
        checkpoint_paths = [output_dir / ('checkpoint_decoder-%s.pth' % epoch_name)]
        for checkpoint_path in checkpoint_paths:
            to_save = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'epoch': epoch,
                'scaler': loss_scaler.state_dict(),
                'args': args,
            }

            if model_ema is not None:
                to_save['model_ema'] = get_state_dict(model_ema)

            save_on_master(to_save, checkpoint_path)
    else:
        client_state = {'epoch': epoch}
        if model_ema is not None:
            client_state['model_ema'] = get_state_dict(model_ema)
        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint_decoder-%s" % epoch_name, client_state=client_state)


def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
    """
    Re-start from checkpoint
    """
    if not os.path.isfile(ckp_path):
        return
    print("Found checkpoint at {}".format(ckp_path))

    # open checkpoint file
    checkpoint = torch.load(ckp_path, map_location="cpu")

    # key is what to look for in the checkpoint file
    # value is the object to load
    # example: {'state_dict': model}
    for key, value in kwargs.items():
        if key in checkpoint and value is not None:
            try:
                msg = value.load_state_dict(checkpoint[key], strict=False)
                print("=> loaded '{}' from checkpoint '{}' with msg {}".format(key, ckp_path, msg))
            except TypeError:
                try:
                    msg = value.load_state_dict(checkpoint[key])
                    print("=> loaded '{}' from checkpoint: '{}'".format(key, ckp_path))
                except ValueError:
                    print("=> failed to load '{}' from checkpoint: '{}'".format(key, ckp_path))
        else:
            print("=> key '{}' not found in checkpoint: '{}'".format(key, ckp_path))

    # re load variable important for the run
    if run_variables is not None:
        for var_name in run_variables:
            if var_name in checkpoint:
                run_variables[var_name] = checkpoint[var_name]


def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_resume = True):
    output_dir = Path(args.output_dir)
    if loss_scaler is not None:
        # torch.amp
        if args.auto_resume and len(args.resume) == 0:
            import glob
            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')
            #print(checkpoint['model'].keys())
            model_without_ddp.load_state_dict(checkpoint['model'])
            if optimizer_resume:
                print("Resume checkpoint %s" % args.resume)
                if 'optimizer' in checkpoint and 'epoch' in checkpoint:
                    optimizer.load_state_dict(checkpoint['optimizer'])
                    args.start_epoch = checkpoint['epoch'] + 1
                    if hasattr(args, 'model_ema') and args.model_ema:
                        _load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
                    if 'scaler' in checkpoint:
                        loss_scaler.load_state_dict(checkpoint['scaler'])
                    print("With optim & sched!")
    else:
        # deepspeed, only support '--auto_resume'.
        if args.auto_resume:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*'))
            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' % latest_ckpt)
                print("Auto resume checkpoint: %d" % latest_ckpt)
                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint-%d' % latest_ckpt)
                args.start_epoch = client_states['epoch'] + 1
                if model_ema is not None:
                    if args.model_ema:
                        _load_checkpoint_for_ema(model_ema, client_states['model_ema'])

def auto_load_model_copy(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_resume = True):
    output_dir = Path(args.output_dir)
    if loss_scaler is not None:
        # torch.amp
        if args.auto_resume and len(args.resume_copy) == 0:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint_copy-*.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_copy = os.path.join(output_dir, 'checkpoint_copy-%d.pth' % latest_ckpt)
            print("Auto resume_copy checkpoint: %s" % args.resume_copy)

        if args.resume_copy:
            if args.resume_copy.startswith('https'):
                checkpoint = torch.hub.load_state_dict_from_url(
                    args.resume_copy, map_location='cpu', check_hash=True)
            else:
                checkpoint = torch.load(args.resume_copy, map_location='cpu')
            print('---------checkpoint_copy------------')
            print(checkpoint['model'].keys())
            model_without_ddp.load_state_dict(checkpoint['model'])
            if optimizer_resume:
                print("Resume checkpoint %s" % args.resume_copy)
                if 'optimizer' in checkpoint and 'epoch' in checkpoint:
                    optimizer.load_state_dict(checkpoint['optimizer'])
                    args.start_epoch = checkpoint['epoch'] + 1
                    if hasattr(args, 'model_ema') and args.model_ema:
                        _load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
                    if 'scaler' in checkpoint:
                        loss_scaler.load_state_dict(checkpoint['scaler'])
                    print("With optim & sched!")
    else:
        # deepspeed, only support '--auto_resume'.
        if args.auto_resume:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint_copy-*'))
            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_copy = os.path.join(output_dir, 'checkpoint_copy-%d' % latest_ckpt)
                print("Auto resume checkpoint: %d" % latest_ckpt)
                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint_copy-%d' % latest_ckpt)
                args.start_epoch = client_states['epoch'] + 1
                if model_ema is not None:
                    if args.model_ema:
                        _load_checkpoint_for_ema(model_ema, client_states['model_ema'])

def auto_load_decoder(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_resume = True):
    output_dir = Path(args.output_dir)
    if loss_scaler is not None:
        # torch.amp
        if args.auto_resume and len(args.resume_decoder) == 0:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint_decoder-*.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_decoder = os.path.join(output_dir, 'checkpoint_decoder-%d.pth' % latest_ckpt)
            print("Auto resume checkpoint: %s" % args.resume_decoder)

        if args.resume_decoder:
            if args.resume_decoder.startswith('https'):
                checkpoint = torch.hub.load_state_dict_from_url(
                    args.resume_decoder, map_location='cpu', check_hash=True)
            else:
                checkpoint = torch.load(args.resume_decoder, map_location='cpu')
            print('---------checkpoint_decoder------------')
            print(checkpoint['model'].keys())
            model_without_ddp.load_state_dict(checkpoint['model'])
            if optimizer_resume:
                print("Resume checkpoint %s" % args.resume_decoder)
                if 'optimizer' in checkpoint and 'epoch' in checkpoint:
                    optimizer.load_state_dict(checkpoint['optimizer'])
                    args.start_epoch = checkpoint['epoch'] + 1
                    if hasattr(args, 'model_ema') and args.model_ema:
                        _load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
                    if 'scaler' in checkpoint:
                        loss_scaler.load_state_dict(checkpoint['scaler'])
                    print("With optim & sched!")
    else:
        # deepspeed, only support '--auto_resume'.
        if args.auto_resume:
            import glob
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint_decoder-*'))
            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_decoder = os.path.join(output_dir, 'checkpoint_decoder-%d' % latest_ckpt)
                print("Auto resume checkpoint: %d" % latest_ckpt)
                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint_decoder-%d' % latest_ckpt)
                args.start_epoch = client_states['epoch'] + 1
                if model_ema is not None:
                    if args.model_ema:
                        _load_checkpoint_for_ema(model_ema, client_states['model_ema'])


def create_ds_config(args):
    args.deepspeed_config = os.path.join(args.output_dir, "deepspeed_config.json")
    with open(args.deepspeed_config, mode="w") as writer:
        ds_config = {
            "train_batch_size": args.batch_size * args.update_freq * get_world_size(),
            "train_micro_batch_size_per_gpu": args.batch_size,
            "steps_per_print": 1000,
            "optimizer": {
                "type": "Adam",
                "adam_w_mode": True,
                "params": {
                    "lr": args.lr,
                    "weight_decay": args.weight_decay,
                    "bias_correction": True,
                    "betas": [
                        0.9,
                        0.999
                    ],
                    "eps": 1e-8
                }
            },
            "fp16": {
                "enabled": True,
                "loss_scale": 0,
                "initial_scale_power": 7,
                "loss_scale_window": 128
            }
        }

        writer.write(json.dumps(ds_config, indent=2))

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#

def _gather_from_all(tensor):
    """
    Gather tensors from all gpus
    """
    gathered_tensor = [torch.zeros_like(tensor) for _ in range(dist.get_world_size())]
    dist.all_gather(gathered_tensor, tensor)
    gathered_tensor = torch.cat(gathered_tensor, 0)
    return gathered_tensor


class NCECriterion(nn.Module):
    def __init__(self, nLem):
        super(NCECriterion, self).__init__()
        self.nLem = nLem
        self.register_buffer('avg_exp_score', torch.tensor(-1.))
        self.distributed = dist.is_available() and dist.is_initialized()

    def compute_partition_function(self, out):
        if self.avg_exp_score > 0:
            # Use precomputed value
            return self.avg_exp_score

        with torch.no_grad():
            batch_mean = out.mean().unsqueeze(0)
            if self.distributed:
                batch_mean_gathered = _gather_from_all(batch_mean)
                all_batch_mean = batch_mean_gathered.mean().squeeze()
            else:
                all_batch_mean = batch_mean
            Z = all_batch_mean

        self.avg_exp_score = Z
        return self.avg_exp_score

    def forward(self, scores_pos, scores_neg):
        K = scores_neg.size(1)

        # Compute unnormalized distributions
        exp_scores_pos = torch.exp(scores_pos)
        exp_scores_neg = torch.exp(scores_neg)

        # Compute partition function and normalize
        with torch.no_grad():
            avg_exp_score = self.compute_partition_function(exp_scores_neg)

        # eq 5.1 : P(origin=model) = Pmt / (Pmt + k*Pnt)
        Pmt = torch.div(exp_scores_pos, exp_scores_pos + K * avg_exp_score)
        lnPmtSum = -torch.log(Pmt).mean(-1)

        # eq 5.2 : P(origin=noise) = k*Pn / (Pms + k*Pn)
        Pon = torch.div(K * avg_exp_score, exp_scores_neg + K * avg_exp_score)
        lnPonSum = -torch.log(Pon).sum(-1)

        loss = (lnPmtSum + lnPonSum).mean()
        return loss