cosmos_predict1/checkpointer/safe_broadcast.py

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import collections
import io
import pickle
from typing import Any

import torch
import torch.distributed as dist


# https://github.com/pytorch/pytorch/blob/main/torch/distributed/optim/zero_redundancy_optimizer.py#L29
def broadcast_object(
    obj: Any,
    src_rank: int,
    group: object = dist.group.WORLD,
    device: torch.device = torch.device("cpu"),
) -> Any:
    r"""
    Broadcasts an object to the given group.

    It will be sending the object if called from the source rank and receiving
    the object otherwise.

    Arguments:
        obj: object to broadcast; only used if called on the source rank.
        src_rank (int): source rank.
        group (``ProcessGroup``, optional): group used for the broadcast
            (default: ``dist.group.WORLD``).
        device (``torch.device``, optional): device to send from or receive
            to (default: ``torch.device("cpu")``).

    Returns:
        The broadcasted object.
    """
    if dist.get_rank() == src_rank:
        # Send the object
        buffer = io.BytesIO()
        torch.save(obj, buffer, pickle_protocol=pickle.HIGHEST_PROTOCOL)
        data = bytearray(buffer.getbuffer())
        length_tensor = torch.LongTensor([len(data)]).to(device)
        data_send_tensor = torch.ByteTensor(data).to(device)
        dist.broadcast(length_tensor, src=src_rank, group=group, async_op=False)
        dist.broadcast(data_send_tensor, src=src_rank, group=group, async_op=False)
    else:
        # Receive the object
        length_tensor = torch.LongTensor([0]).to(device)
        dist.broadcast(length_tensor, src=src_rank, group=group, async_op=False)
        data_recv_tensor = torch.empty([int(length_tensor.item())], dtype=torch.uint8, device=device)
        dist.broadcast(data_recv_tensor, src=src_rank, group=group, async_op=False)
        buffer = io.BytesIO(data_recv_tensor.cpu().numpy())
        obj = torch.load(buffer, map_location=device, weights_only=False)
    return obj


def _recursive_copy_to_device(
    value: Any,
    non_blocking: bool,
    device: torch.device,
) -> Any:
    r"""
    Recursively searches lists, tuples, dicts and copies tensors to device if possible.

    Non-tensor values are passed as-is in the result.

    .. note:  These are all copies, so if there are two objects that reference
    the same object, then after this call, there will be two different objects
    referenced on the device.
    """
    if isinstance(value, torch.Tensor):
        return value.to(device, non_blocking=non_blocking)

    if isinstance(value, (list, tuple)):
        values = [_recursive_copy_to_device(val, non_blocking=non_blocking, device=device) for val in value]
        return values if isinstance(value, list) else tuple(values)

    if isinstance(value, collections.abc.Mapping):
        return {
            key: _recursive_copy_to_device(val, non_blocking=non_blocking, device=device) for key, val in value.items()
        }

    return value