SMPLer-X/common/utils/distribute_utils.py

import mmcv
import os
import os.path as osp
import pickle
import shutil
import tempfile
import time
import torch
import torch.distributed as dist
from mmengine.dist import get_dist_info
import random
import numpy as np
import subprocess

def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    # torch.set_deterministic(True)


def time_synchronized():
    torch.cuda.synchronize() if torch.cuda.is_available() else None
    return time.time()


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(port = None, master_port=29500):
    """Initialize slurm distributed training environment.

    If argument ``port`` is not specified, then the master port will be system
    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
    environment variable, then a default port ``29500`` will be used.

    Args:
        backend (str): Backend of torch.distributed.
        port (int, optional): Master port. Defaults to None.
    """
    dist_backend = 'nccl'
    proc_id = int(os.environ['SLURM_PROCID'])
    ntasks = int(os.environ['SLURM_NTASKS'])
    node_list = os.environ['SLURM_NODELIST']
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(proc_id % num_gpus)
    addr = subprocess.getoutput(
        f'scontrol show hostname {node_list} | head -n1')
    # specify master port
    if port is not None:
        os.environ['MASTER_PORT'] = str(port)
    elif 'MASTER_PORT' in os.environ:
        pass  # use MASTER_PORT in the environment variable
    else:
        # 29500 is torch.distributed default port
        os.environ['MASTER_PORT'] = str(master_port)
    # use MASTER_ADDR in the environment variable if it already exists
    if 'MASTER_ADDR' not in os.environ:
        os.environ['MASTER_ADDR'] = addr
    os.environ['WORLD_SIZE'] = str(ntasks)
    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
    os.environ['RANK'] = str(proc_id)
    dist.init_process_group(backend=dist_backend)

    distributed = True
    gpu_idx = proc_id % num_gpus

    return distributed, gpu_idx


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 get_process_groups():
    world_size = int(os.environ['WORLD_SIZE'])
    ranks = list(range(world_size))
    num_gpus = torch.cuda.device_count()
    num_nodes = world_size // num_gpus
    if world_size % num_gpus != 0:
        raise NotImplementedError('Not implemented for node not fully used.')

    groups = []
    for node_idx in range(num_nodes):
        groups.append(ranks[node_idx*num_gpus : (node_idx+1)*num_gpus])
    process_groups = [torch.distributed.new_group(group) for group in groups]

    return process_groups

def get_group_idx():
    num_gpus = torch.cuda.device_count()
    proc_id = get_rank()
    group_idx = proc_id // num_gpus

    return group_idx


def is_main_process():
    return get_rank() == 0

def cleanup():
    dist.destroy_process_group()


def collect_results(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, f'part_{i}.pkl')
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results


def all_gather(data):
    """
    Run all_gather on arbitrary picklable data (not necessarily tensors)
    Args:
        data:
            Any picklable object
    Returns:
        data_list(list):
            List of data gathered from each rank
    """
    world_size = get_world_size()
    if world_size == 1:
        return [data]

    # serialized to a Tensor
    buffer = pickle.dumps(data)
    storage = torch.ByteStorage.from_buffer(buffer)
    tensor = torch.ByteTensor(storage).to('cuda')

    # obtain Tensor size of each rank
    local_size = torch.tensor([tensor.numel()], device='cuda')
    size_list = [torch.tensor([0], device='cuda') for _ in range(world_size)]
    dist.all_gather(size_list, local_size)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)

    # 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='cuda'))
    if local_size != max_size:
        padding = torch.empty(
            size=(max_size - local_size, ), dtype=torch.uint8, device='cuda')
        tensor = torch.cat((tensor, padding), dim=0)
    dist.all_gather(tensor_list, tensor)

    data_list = []
    for size, tensor in zip(size_list, tensor_list):
        buffer = tensor.cpu().numpy().tobytes()[:size]
        data_list.append(pickle.loads(buffer))

    return data_list