test_distributed_backward_transform.py

# coding=utf-8

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# we need this in order to enable distributed
import torch
import torch.distributed as dist

# those need to be global
_POLAR_PARALLEL_GROUP = None
_AZIMUTH_PARALLEL_GROUP = None
_IS_INITIALIZED = False

def polar_group():
    return _POLAR_PARALLEL_GROUP

def azimuth_group():
    return _AZIMUTH_PARALLEL_GROUP

def init(polar_process_group, azimuth_process_group):
    global _POLAR_PARALLEL_GROUP
    global _AZIMUTH_PARALLEL_GROUP
    _POLAR_PARALLEL_GROUP = polar_process_group
    _AZIMUTH_PARALLEL_GROUP = azimuth_process_group
    _IS_INITIALIZED = True

def is_initialized() -> bool:
    return _IS_INITIALIZED

def is_distributed_polar() -> bool:
    return (_POLAR_PARALLEL_GROUP is not None)

def is_distributed_azimuth() -> bool:
    return (_AZIMUTH_PARALLEL_GROUP is not None)

def polar_group_size() -> int:
    if not is_distributed_polar():
        return 1
    else:
        return dist.get_world_size(group = _POLAR_PARALLEL_GROUP)

def azimuth_group_size() -> int:
    if not is_distributed_azimuth():
        return 1
    else:
        return dist.get_world_size(group = _AZIMUTH_PARALLEL_GROUP)

def polar_group_rank() -> int:
    if not is_distributed_polar():
        return 0
    else:
        return dist.get_rank(group = _POLAR_PARALLEL_GROUP)

def azimuth_group_rank() -> int:
    if not is_distributed_azimuth():
        return 0
    else:
        return dist.get_rank(group = _AZIMUTH_PARALLEL_GROUP)