utils.py

import numpy as np
import os
import shutil
import torch
import torch.distributed as dist


def get_model(model):
    if isinstance(model, torch.nn.DataParallel) \
      or isinstance(model, torch.nn.parallel.DistributedDataParallel):
        return model.module
    else:
        return model


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(state, is_best, output_dir):
    if is_main_process():
        ckpt_path = f'{output_dir}/checkpoint.pt'
        best_path = f'{output_dir}/checkpoint_best.pt'
        torch.save(state, ckpt_path)
        if is_best:
            shutil.copyfile(ckpt_path, best_path)


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 {}): {}'.format(
        args.rank, args.dist_url), 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 scaled_all_reduce(tensors, is_scale=True):
    """Performs the scaled all_reduce operation on the provided tensors.
    The input tensors are modified in-place. Currently supports only the sum
    reduction operator. The reduced values are scaled by the inverse size of the
    world size.
    """
    world_size = get_world_size()
    # There is no need for reduction in the single-proc case
    if world_size == 1:
        return tensors
    # Queue the reductions
    reductions = []
    for tensor in tensors:
        reduction = dist.all_reduce(tensor, async_op=True)
        reductions.append(reduction)
    # Wait for reductions to finish
    for reduction in reductions:
        reduction.wait()
    # Scale the results
    if is_scale:
        for tensor in tensors:
            tensor.mul_(1.0 / world_size)
    return tensors


def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0):
    warmup_schedule = np.array([])
    warmup_iters = warmup_epochs * niter_per_ep
    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 = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters)))

    schedule = np.concatenate((warmup_schedule, schedule))
    assert len(schedule) == epochs * niter_per_ep
    return schedule