maskrcnn_benchmark/utils/mdetr_dist.py

# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""

Utilities related to distributed mode.



By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we reuse the NCCL backend)

If you want to reduce on CPU instead (required for big datasets like GQA), use the env variable MDETR_CPU_REDUCE=1

"""
import functools
import io
import os
import datetime

import torch
import torch.distributed as dist

_LOCAL_PROCESS_GROUP = None


@functools.lru_cache()
def _get_global_gloo_group():
    """

    Return a process group based on gloo backend, containing all the ranks

    The result is cached.

    """

    if dist.get_backend() == "nccl":
        return dist.new_group(backend="gloo")

    return dist.group.WORLD

def all_gather(data):
    """

    Run all_gather on arbitrary picklable data (not necessarily tensors)

    Args:

        data: any picklable object

    Returns:

        list[data]: list of data gathered from each rank

    """

    world_size = get_world_size()
    if world_size == 1:
        return [data]

    cpu_group = None
    if os.getenv("MDETR_CPU_REDUCE") == "1":
        cpu_group = _get_global_gloo_group()

    buffer = io.BytesIO()
    torch.save(data, buffer)
    data_view = buffer.getbuffer()
    device = "cuda" if cpu_group is None else "cpu"
    tensor = torch.ByteTensor(data_view).to(device)

    # obtain Tensor size of each rank
    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
    if cpu_group is None:
        dist.all_gather(size_list, local_size)
    else:
        print("gathering on cpu")
        dist.all_gather(size_list, local_size, group=cpu_group)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)
    assert isinstance(local_size.item(), int)
    local_size = int(local_size.item())

    # receiving Tensor from all ranks
    # we pad the tensor because torch all_gather does not support
    # gathering tensors of different shapes
    tensor_list = []
    for _ in size_list:
        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
    if local_size != max_size:
        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
        tensor = torch.cat((tensor, padding), dim=0)
    if cpu_group is None:
        dist.all_gather(tensor_list, tensor)
    else:
        dist.all_gather(tensor_list, tensor, group=cpu_group)

    data_list = []
    for size, tensor in zip(size_list, tensor_list):
        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
        buffer = io.BytesIO(tensor.cpu().numpy())
        obj = torch.load(buffer)
        data_list.append(obj)

    return data_list


def reduce_dict(input_dict, average=True):
    """

    Args:

        input_dict (dict): all the values will be reduced

        average (bool): whether to do average or sum

    Reduce the values in the dictionary from all processes so that all processes

    have the averaged results. Returns a dict with the same fields as

    input_dict, after reduction.

    """
    world_size = get_world_size()
    if world_size < 2:
        return input_dict
    with torch.no_grad():
        names = []
        values = []
        # sort the keys so that they are consistent across processes
        for k in sorted(input_dict.keys()):
            names.append(k)
            values.append(input_dict[k])
        values = torch.stack(values, dim=0)
        dist.all_reduce(values)
        if average:
            values /= world_size
        reduced_dict = {k: v for k, v in zip(names, values)}
    return reduced_dict


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():
    """

    Returns:

        True if distributed training is enabled

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


def get_world_size():
    """

    Returns:

        The number of processes in the process group

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


def get_rank():
    """

    Returns:

        The rank of the current process within the global process group.

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


def get_local_rank() -> int:
    """

    Returns:

        The rank of the current process within the local (per-machine) process group.

    """
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    assert _LOCAL_PROCESS_GROUP is not None
    return dist.get_rank(group=_LOCAL_PROCESS_GROUP)


def get_local_size() -> int:
    """

    Returns:

        The size of the per-machine process group,

        i.e. the number of processes per machine.

    """
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)


def is_main_process():
    """Return true if the current process is the main one"""
    return get_rank() == 0


def save_on_master(*args, **kwargs):
    """Utility function to save only from the main process"""
    if is_main_process():
        torch.save(*args, **kwargs)


def init_distributed_mode(args):
    """Initialize distributed training, if appropriate"""
    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)

    dist.init_process_group(
        backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank,
        timeout=datetime.timedelta(0, 7200)
    )
    dist.barrier()
    setup_for_distributed(args.debug or args.rank == 0)