utils.py

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

import datetime
import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from collections import OrderedDict
from typing import Any, Dict, List, Mapping, Optional
from dataclasses import dataclass

import torch
import torch.distributed as dist


logger = logging.getLogger(__name__)


def is_master(args):
    return args.distributed_rank == 0


def infer_init_method(args, force_distributed=False):
    if args.distributed_init_method is not None:
        return

    if all(
        key in os.environ
        for key in ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"]
    ):
        # support torch.distributed.launch
        _infer_torch_distributed_launch_init(args)
    elif args.distributed_port > 0:
        # we can determine the init method automatically for Slurm
        _infer_slurm_init(args)
    elif args.distributed_world_size > 1 or force_distributed:
        # fallback for single node with multiple GPUs
        _infer_single_node_init(args)

    elif not args.distributed_no_spawn:
        args.distributed_num_procs = min(
            torch.cuda.device_count(), args.distributed_world_size
        )


def _infer_torch_distributed_launch_init(args):
    args.distributed_init_method = "env://"
    args.distributed_world_size = int(os.environ["WORLD_SIZE"])
    args.distributed_rank = int(os.environ["RANK"])
    # processes are created by torch.distributed.launch
    args.distributed_no_spawn = True


def _infer_slurm_init(args):
    node_list = os.environ.get("SLURM_STEP_NODELIST")
    if node_list is None:
        node_list = os.environ.get("SLURM_JOB_NODELIST")
    if node_list is not None:
        try:
            hostnames = subprocess.check_output(
                ["scontrol", "show", "hostnames", node_list]
            )
            args.distributed_init_method = "tcp://{host}:{port}".format(
                host=hostnames.split()[0].decode("utf-8"),
                port=args.distributed_port,
            )
            nnodes = int(os.environ.get("SLURM_NNODES"))
            ntasks_per_node = os.environ.get("SLURM_NTASKS_PER_NODE")
            if ntasks_per_node is not None:
                ntasks_per_node = int(ntasks_per_node)
            else:
                ntasks = int(os.environ.get("SLURM_NTASKS"))
                nnodes = int(os.environ.get("SLURM_NNODES"))
                assert ntasks % nnodes == 0
                ntasks_per_node = int(ntasks / nnodes)
            if ntasks_per_node == 1:
                gpus_per_node = torch.cuda.device_count()
                node_id = int(os.environ.get("SLURM_NODEID"))
                args.distributed_rank = node_id * gpus_per_node
                args.distributed_world_size = nnodes * gpus_per_node
            else:
                assert ntasks_per_node == args.distributed_world_size // nnodes
                args.distributed_no_spawn = True
                args.distributed_rank = int(os.environ.get("SLURM_PROCID"))
                args.device_id = int(os.environ.get("SLURM_LOCALID"))
        except subprocess.CalledProcessError as e:  # scontrol failed
            raise e
        except FileNotFoundError:  # Slurm is not installed
            pass


def _infer_single_node_init(args):
    assert (
        args.distributed_world_size <= torch.cuda.device_count()
    ), f"world size is {args.distributed_world_size} but have {torch.cuda.device_count()} available devices"
    port = random.randint(10000, 20000)
    args.distributed_init_method = "tcp://localhost:{port}".format(port=port)



def distributed_init(args):
    if torch.distributed.is_available() and torch.distributed.is_initialized():
        warnings.warn(
            "Distributed is already initialized, cannot initialize twice!"
        )
    else:
        logger.info(
            "distributed init (rank {}): {}".format(
                args.distributed_rank,
                args.distributed_init_method,
            )
        )
        dist.init_process_group(
            backend=args.distributed_backend,
            init_method=args.distributed_init_method,
            world_size=args.distributed_world_size,
            rank=args.distributed_rank,
            timeout=datetime.timedelta(seconds=30),
        )
        logger.info(
            "initialized host {} as rank {}".format(
                socket.gethostname(),
                args.distributed_rank,
            )
        )

        # perform a dummy all-reduce to initialize the NCCL communicator
        if torch.cuda.is_available():
            dist.all_reduce(torch.zeros(1).cuda())

    args.distributed_rank = torch.distributed.get_rank()

    if is_master(args):
        logging.getLogger().setLevel(logging.INFO)
    else:
        logging.getLogger().setLevel(logging.WARNING)


    return args.distributed_rank


def distributed_main(i, main, args, kwargs):
    args.device_id = i
    if torch.cuda.is_available() and not args.cpu:
        torch.cuda.set_device(args.device_id)
    if args.distributed_rank is None:  # torch.multiprocessing.spawn
        args.distributed_rank = kwargs.pop("start_rank", 0) + i

    args.distributed_rank = distributed_init(args)

    after_distributed_init_fn = kwargs.pop("after_distributed_init_fn", None)
    if after_distributed_init_fn:
        args = after_distributed_init_fn(args)

    main(args, **kwargs)

    if torch.distributed.is_initialized():
        torch.distributed.barrier(get_global_group())


def call_main(args, main, **kwargs):
    if args.distributed_init_method is None:
        infer_init_method(args)

    if args.distributed_init_method is not None:
        # distributed training
        if not args.distributed_no_spawn:
            start_rank = args.distributed_rank
            args.distributed_rank = None  # assign automatically
            kwargs["start_rank"] = start_rank
            torch.multiprocessing.spawn(
                fn=distributed_main,
                args=(main, args, kwargs),
                nprocs=min(
                    torch.cuda.device_count(),
                    args.distributed_world_size,
                ),
                join=True,
            )
        else:
            distributed_main(args.device_id, main, args, kwargs)
    else:
        # single GPU main
        main(args, **kwargs)


def new_groups(grouped_ranks: List[List[int]]):
    groups = [dist.new_group(g) for g in grouped_ranks]
    my_group_idx = _find_my_group_index(grouped_ranks)
    return groups[my_group_idx]


def _find_my_group_index(grouped_ranks):
    my_rank = get_global_rank()
    for i, group in enumerate(grouped_ranks):
        if my_rank in group:
            return i
    raise RuntimeError


def _find_my_group(grouped_ranks):
    index = _find_my_group_index(grouped_ranks)
    return grouped_ranks[index]


def get_rank(group):
    return dist.get_rank(group=group)


def get_world_size(group):
    if torch.distributed.is_initialized():
        return dist.get_world_size(group=group)
    else:
        return 1


def get_global_group():
    if torch.distributed.is_initialized():
        if not hasattr(get_global_group, "_global_group"):
            # ideally we could use torch.distributed.group.WORLD, but it seems
            # to cause random NCCL hangs in some cases
            get_global_group._global_group = dist.new_group()
        return get_global_group._global_group
    else:
        return None


def get_global_rank():
    if torch.distributed.is_initialized():
        return torch.distributed.get_rank()
    else:
        return 0


def get_global_world_size():
    if torch.distributed.is_initialized():
        return torch.distributed.get_world_size()
    else:
        return 1


def get_data_parallel_group():
    """Get the data parallel group the caller rank belongs to."""
    return get_global_group()


def get_data_parallel_rank():
    """Return my rank for the data parallel group."""
    return get_rank(get_data_parallel_group())


def get_data_parallel_world_size():
    """Return world size for the data parallel group."""
    return get_world_size(get_data_parallel_group())


def all_reduce(tensor, group, op="sum"):
    if op == "sum":
        op = dist.ReduceOp.SUM
    elif op == "max":
        op = dist.ReduceOp.MAX
    else:
        raise NotImplementedError
    dist.all_reduce(tensor, op=op, group=group)
    return tensor


def broadcast(tensor, src, group):
    dist.broadcast(tensor, src=src, group=group)


def all_to_all(tensor, group):
    """Perform an all-to-all operation on a 1D Tensor."""
    assert tensor.dim() == 1
    split_count = get_world_size(group=group)
    assert tensor.numel() % split_count == 0
    output = torch.zeros_like(tensor)
    dist.all_to_all_single(output, tensor, group=group)
    return output


def all_gather(tensor, group, return_tensor=False):
    """Perform an all-gather operation."""
    world_size = get_world_size(group=group)
    rank = get_rank(group=group)
    tensor_list = [
        tensor if i == rank else torch.empty_like(tensor) for i in range(world_size)
    ]
    dist.all_gather(tensor_list, tensor, group=group)
    if return_tensor:
        return torch.stack(tensor_list, dim=0)
    else:
        return tensor_list


def all_gather_list(data, group=None, max_size=16384):
    """Gathers arbitrary data from all nodes into a list.

    Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
    data. Note that *data* must be picklable and any CUDA tensors will be moved
    to CPU and returned on CPU as well.

    Args:
        data (Any): data from the local worker to be gathered on other workers
        group: group of the collective
        max_size (int, optional): maximum size of the data to be gathered
            across workers
    """
    from unicore import utils

    if group is None:
        group = get_global_group()
    rank = get_rank(group=group)
    world_size = get_world_size(group=group)

    buffer_size = max_size * world_size
    if (
        not hasattr(all_gather_list, "_buffer")
        or all_gather_list._buffer.numel() < buffer_size
    ):
        all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size)
        all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory()
    buffer = all_gather_list._buffer
    buffer.zero_()
    cpu_buffer = all_gather_list._cpu_buffer

    data = utils.move_to_cpu(data)
    enc = pickle.dumps(data)
    enc_size = len(enc)
    header_size = 4  # size of header that contains the length of the encoded data
    size = header_size + enc_size
    if size > max_size:
        raise ValueError(
            "encoded data size ({}) exceeds max_size ({})".format(size, max_size)
        )

    header = struct.pack(">I", enc_size)
    cpu_buffer[:size] = torch.ByteTensor(list(header + enc))
    start = rank * max_size
    buffer[start : start + size].copy_(cpu_buffer[:size])

    all_reduce(buffer, group=group)

    buffer = buffer.cpu()
    try:
        result = []
        for i in range(world_size):
            out_buffer = buffer[i * max_size : (i + 1) * max_size]
            (enc_size,) = struct.unpack(">I", bytes(out_buffer[:header_size].tolist()))
            if enc_size > 0:
                result.append(
                    pickle.loads(
                        bytes(out_buffer[header_size : header_size + enc_size].tolist())
                    )
                )
        return result
    except pickle.UnpicklingError:
        raise Exception(
            "Unable to unpickle data from other workers. all_gather_list requires all "
            "workers to enter the function together, so this error usually indicates "
            "that the workers have fallen out of sync somehow. Workers can fall out of "
            "sync if one of them runs out of memory, or if there are other conditions "
            "in your training script that can cause one worker to finish an epoch "
            "while other workers are still iterating over their portions of the data. "
            "Try rerunning with --ddp-backend=legacy_ddp and see if that helps."
        )


def all_reduce_dict(data: Mapping[str, Any], device, group) -> Dict[str, Any]:
    """
    AllReduce a dictionary of values across workers. We separately
    reduce items that are already on the device and items on CPU for
    better performance.

    Args:
        data (Mapping[str, Any]): dictionary of data to all-reduce, but
            cannot be a nested dictionary
        device (torch.device): device for the reduction
        group: group of the collective
    """
    data_keys = list(data.keys())

    # We want to separately reduce items that are already on the
    # device and items on CPU for performance reasons.
    cpu_data = OrderedDict()
    device_data = OrderedDict()
    for k in data_keys:
        t = data[k]
        if not torch.is_tensor(t):
            cpu_data[k] = torch.tensor(t, dtype=torch.double)
        elif t.device.type != device.type:
            cpu_data[k] = t.to(dtype=torch.double)
        else:
            device_data[k] = t.to(dtype=torch.double)

    def _all_reduce_dict(data: OrderedDict):
        if len(data) == 0:
            return data
        buf = torch.cat([t.view(-1) for t in data.values()]).to(device=device)
        all_reduce(buf, group=group)
        split_buf = torch.split(buf, [t.numel() for t in data.values()])
        reduced_data = [t.view_as(orig) for t, orig in zip(split_buf, data.values())]
        return OrderedDict(zip(data.keys(), reduced_data))

    cpu_data = _all_reduce_dict(cpu_data)
    device_data = _all_reduce_dict(device_data)

    def get_from_stack(key):
        if key in cpu_data:
            return cpu_data[key]
        elif key in device_data:
            return device_data[key]
        raise KeyError

    return OrderedDict([(key, get_from_stack(key)) for key in data_keys])


@dataclass
class _TensorPlaceholder:
    index: int


def broadcast_tensors(
    tensors: Optional[List[torch.Tensor]],
    src_rank: int,
    group: object,
    dist_device: Optional[torch.device] = None,
) -> List[torch.Tensor]:
    """
    Broadcasts a list of tensors without other (non-src) ranks needing to know
    the dtypes/shapes of the tensors.
    """
    if dist_device is None:
        if torch.distributed.get_backend(group) == "nccl":
            dist_device = torch.device("cuda")
        else:
            dist_device = torch.device("cpu")

    # share metadata first to simplify transfer
    is_src_rank = get_rank(group) == src_rank
    if is_src_rank:
        metadata = [
            {"size": t.size(), "dtype": t.dtype, "device": t.device} for t in tensors
        ]
        metadata = _broadcast_object_slow(metadata, src_rank, group, dist_device)
    else:
        metadata = _broadcast_object_slow(None, src_rank, group, dist_device)

    out_tensors = []
    for i, meta in enumerate(metadata):
        if is_src_rank:
            tensor = tensors[i]
            broadcast(tensors[i].to(dist_device), src=src_rank, group=group)
        else:
            tensor = torch.zeros(
                [meta["size"].numel()], dtype=meta["dtype"], device=dist_device
            )
            broadcast(tensor, src=src_rank, group=group)
        tensor = tensor.view(meta["size"]).to(meta["device"])
        out_tensors.append(tensor)
    return out_tensors


def broadcast_object(
    obj: Any,
    src_rank: int,
    group: object,
    dist_device: Optional[torch.device] = None,
) -> Any:
    """Broadcast an arbitrary Python object to other workers."""
    if dist_device is None:
        if torch.distributed.get_backend(group) == "nccl":
            dist_device = torch.device("cuda")
        else:
            dist_device = torch.device("cpu")

    if get_rank(group) == src_rank:
        # split the tensors from the non-tensors so we can broadcast them
        # directly, avoiding unnecessary serialization/deserialization
        tensors = []
        obj = _split_tensors_from_obj(obj, tensors)
        obj = _broadcast_object_slow(obj, src_rank, group, dist_device)
        tensors = broadcast_tensors(tensors, src_rank, group, dist_device)
    else:
        obj = _broadcast_object_slow(None, src_rank, group, dist_device)
        tensors = broadcast_tensors(None, src_rank, group, dist_device)
    return _put_tensors_in_obj(obj, tensors)


def _broadcast_object_slow(
    obj: Any,
    src_rank: int,
    group: object,
    dist_device: torch.device,
) -> Any:
    if get_rank(group) == src_rank:
        # Emit data
        buffer = io.BytesIO()
        torch.save(obj, buffer)
        buffer = torch.ByteTensor(buffer.getbuffer()).to(dist_device)
        length = torch.LongTensor([len(buffer)]).to(dist_device)
        broadcast(length, src=src_rank, group=group)
        broadcast(buffer, src=src_rank, group=group)
    else:
        # Fetch from the source
        length = torch.LongTensor([0]).to(dist_device)
        broadcast(length, src=src_rank, group=group)
        buffer = torch.ByteTensor(int(length.item())).to(dist_device)
        broadcast(buffer, src=src_rank, group=group)
        buffer = io.BytesIO(buffer.cpu().numpy())
        obj = torch.load(buffer, map_location="cpu")
    return obj


def _split_tensors_from_obj(obj: Any, tensors: List[torch.Tensor]) -> Any:
    if torch.is_tensor(obj):
        placeholder = _TensorPlaceholder(index=len(tensors))
        tensors.append(obj)
        return placeholder
    elif isinstance(obj, dict):
        return {k: _split_tensors_from_obj(v, tensors) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [_split_tensors_from_obj(v, tensors) for v in obj]
    elif isinstance(obj, tuple):
        return tuple(_split_tensors_from_obj(v, tensors) for v in obj)
    elif isinstance(obj, set):
        return {_split_tensors_from_obj(v, tensors) for v in obj}
    else:
        return obj


def _put_tensors_in_obj(obj: Any, tensors: List[torch.Tensor]) -> Any:
    if isinstance(obj, _TensorPlaceholder):
        return tensors[obj.index]
    elif isinstance(obj, dict):
        return {k: _put_tensors_in_obj(v, tensors) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [_put_tensors_in_obj(v, tensors) for v in obj]
    elif isinstance(obj, tuple):
        return tuple(_put_tensors_in_obj(v, tensors) for v in obj)
    elif isinstance(obj, set):
        return {_put_tensors_in_obj(v, tensors) for v in obj}
    else:
        return obj