verl/protocol.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

Implement base data transfer protocol between any two functions, modules.

We can subclass Protocol to define more detailed batch info with specific keys

"""

import contextlib
import copy
import logging
import os
import pickle
from dataclasses import dataclass, field
from typing import Callable, Dict, List, Union

import numpy as np
import pandas as pd
import ray
import tensordict
import torch
import torch.distributed
from packaging import version
from tensordict import TensorDict
from torch.utils.data import DataLoader

from verl.utils.py_functional import union_two_dict
from verl.utils.torch_functional import allgather_dict_tensors

__all__ = ["DataProto", "union_tensor_dict"]

with contextlib.suppress(Exception):
    tensordict.set_lazy_legacy(False).set()


class _DataProtoConfigMeta(type):
    _config = {}

    auto_padding_key = "_verl_auto_padding"

    @property
    def auto_padding(cls):
        enabled_by_env = os.getenv("VERL_AUTO_PADDING", "FALSE").upper() in ["TRUE", "1"]
        return enabled_by_env or cls._config.get(cls.auto_padding_key, False)

    @auto_padding.setter
    def auto_padding(cls, enabled: bool):
        assert isinstance(enabled, bool), f"enabled must be a boolean, got {enabled} as {type(enabled)}"
        cls._config[cls.auto_padding_key] = enabled


class DataProtoConfig(metaclass=_DataProtoConfigMeta):
    pass


_padding_size_key = "_padding_size_key_x123d"


def pad_dataproto_to_divisor(data: "DataProto", size_divisor: int):
    """Pad a DataProto to size divisible by size_divisor



    Args:

        size_divisor (int): size divisor



    Returns:

        data: (DataProto): the padded DataProto

        pad_size (int)

    """
    assert isinstance(data, DataProto), "data must be a DataProto"
    if len(data) % size_divisor != 0:
        pad_size = size_divisor - len(data) % size_divisor
        padding_protos = []
        remaining_pad = pad_size
        while remaining_pad > 0:
            take_size = min(remaining_pad, len(data))
            padding_protos.append(data[:take_size])
            remaining_pad -= take_size
        data_padded = DataProto.concat([data] + padding_protos)
    else:
        if len(data) == 0:
            logging.warning("padding a DataProto with no item, no changed made")
        pad_size = 0
        data_padded = data
    return data_padded, pad_size


def unpad_dataproto(data: "DataProto", pad_size):
    if pad_size != 0:
        data = data[:-pad_size]
    return data


def union_tensor_dict(tensor_dict1: TensorDict, tensor_dict2: TensorDict) -> TensorDict:
    """Union two tensordicts."""
    assert tensor_dict1.batch_size == tensor_dict2.batch_size, f"Two tensor dict must have identical batch size. Got {tensor_dict1.batch_size} and {tensor_dict2.batch_size}"
    for key in tensor_dict2.keys():
        if key not in tensor_dict1.keys():
            tensor_dict1[key] = tensor_dict2[key]
        else:
            assert tensor_dict1[key].equal(tensor_dict2[key]), f"{key} in tensor_dict1 and tensor_dict2 are not the same object"

    return tensor_dict1


def union_numpy_dict(tensor_dict1: dict[str, np.ndarray], tensor_dict2: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
    for key, val in tensor_dict2.items():
        if key in tensor_dict1:
            assert isinstance(tensor_dict2[key], np.ndarray)
            assert isinstance(tensor_dict1[key], np.ndarray)
            # to properly deal with nan and object type
            assert pd.DataFrame(tensor_dict2[key]).equals(pd.DataFrame(tensor_dict1[key])), f"{key} in tensor_dict1 and tensor_dict2 are not the same object"
        tensor_dict1[key] = val

    return tensor_dict1


def list_of_dict_to_dict_of_list(list_of_dict: list[dict]):
    if len(list_of_dict) == 0:
        return {}
    keys = list_of_dict[0].keys()
    output = {key: [] for key in keys}
    for data in list_of_dict:
        for key, item in data.items():
            assert key in output
            output[key].append(item)
    return output


def fold_batch_dim(data: "DataProto", new_batch_size):
    """

    Fold a batch dim from [bsz, xxx] into [new_bsz, bsz // new_bsz, xxx]

    """
    batch_size = data.batch.batch_size[0]

    assert batch_size % new_batch_size == 0

    tensor: TensorDict = data.batch
    non_tensor = data.non_tensor_batch

    tensor = tensor.view(new_batch_size, -1)
    tensor.auto_batch_size_(batch_dims=1)

    for key, val in non_tensor.items():
        non_tensor[key] = np.reshape(val, newshape=(new_batch_size, -1, *val.shape[1:]))

    return type(data)(batch=tensor, non_tensor_batch=non_tensor, meta_info=data.meta_info)


def unfold_batch_dim(data: "DataProto", batch_dims=2):
    """

    Unfold the first n dims as new batch dim

    """
    tensor: TensorDict = data.batch
    non_tensor = data.non_tensor_batch
    tensor.auto_batch_size_(batch_dims=batch_dims)
    tensor = tensor.view(-1)

    batch_size = tensor.batch_size[0]

    non_tensor_new = {}

    for key, val in non_tensor.items():
        non_tensor_new[key] = np.reshape(val, newshape=(batch_size, *val.shape[batch_dims:]))

    return type(data)(batch=tensor, non_tensor_batch=non_tensor_new, meta_info=data.meta_info)


def collate_fn(x: list["DataProtoItem"]):
    batch = []
    non_tensor_batch = []
    for data in x:
        batch.append(data.batch)
        non_tensor_batch.append(data.non_tensor_batch)
    batch = torch.stack(batch).contiguous()
    non_tensor_batch = list_of_dict_to_dict_of_list(non_tensor_batch)
    for key, val in non_tensor_batch.items():
        non_tensor_batch[key] = np.array(val, dtype=object)
    return DataProto(batch=batch, non_tensor_batch=non_tensor_batch)


@dataclass
class DataProtoItem:
    # TODO(zhangchi.usc1992) add consistency check
    batch: TensorDict = None
    non_tensor_batch: Dict = field(default_factory=dict)
    meta_info: Dict = field(default_factory=dict)


@dataclass
class DataProto:
    """

    A DataProto is a data structure that aims to provide a standard protocol for data exchange between functions.

    It contains a batch (TensorDict) and a meta_info (Dict). The batch is a TensorDict https://pytorch.org/tensordict/.

    TensorDict allows you to manipulate a dictionary of Tensors like a single Tensor. Ideally, the tensors with the

    same batch size should be put inside batch.

    """

    batch: TensorDict = None
    non_tensor_batch: Dict = field(default_factory=dict)
    meta_info: Dict = field(default_factory=dict)

    def __post_init__(self):
        # perform necessary checking
        self.check_consistency()

    def __len__(self):
        if self.batch is not None:
            return self.batch.batch_size[0]
        elif self.non_tensor_batch is not None and len(self.non_tensor_batch) > 0:
            random_key = list(self.non_tensor_batch.keys())[0]
            return self.non_tensor_batch[random_key].shape[0]
        else:
            return 0

    def __getitem__(self, item):
        """

        Enhanced indexing for DataProto objects.



        Args:

            item: Can be one of:

                - int: A single index

                - slice: A slice object (start:stop:step)

                - list: A list of indices

                - numpy.ndarray: An array of indices

                - torch.Tensor: A tensor of indices



        Returns:

            DataProto: For all indexing types except single integers

            DataProtoItem: Only for single integer indices

        """
        # Case 1: Slice object - use the slice method
        if isinstance(item, slice):
            return self.slice(item.start, item.stop, item.step)

        # Case 2: List, numpy array, or torch tensor - use sel_idxs
        elif isinstance(item, (list, np.ndarray, torch.Tensor)):
            return self.select_idxs(item)

        # Case 3: Single integer - return DataProtoItem for backward compatibility
        elif isinstance(item, (int, np.integer)):
            tensor_data = self.batch[item]
            non_tensor_data = {key: val[item] for key, val in self.non_tensor_batch.items()}
            return DataProtoItem(batch=tensor_data, non_tensor_batch=non_tensor_data, meta_info=self.meta_info)

        # # Case 4: Unsupported type
        else:
            raise TypeError(f"Indexing with {type(item)} is not supported")

    def __getstate__(self):
        import io

        buffer = io.BytesIO()
        if version.parse(tensordict.__version__) >= version.parse("0.5.0") and self.batch is not None:
            self.batch = self.batch.contiguous()
            self.batch = self.batch.consolidate()
        torch.save(self.batch, buffer)
        buffer_bytes = buffer.getvalue()
        return buffer_bytes, self.non_tensor_batch, self.meta_info

    def __setstate__(self, data):
        import io

        batch_deserialized_bytes, non_tensor_batch, meta_info = data
        batch_deserialized = io.BytesIO(initial_bytes=batch_deserialized_bytes)
        batch = torch.load(batch_deserialized, weights_only=False, map_location="cpu" if not torch.cuda.is_available() else None)
        self.batch = batch
        self.non_tensor_batch = non_tensor_batch
        self.meta_info = meta_info

    def save_to_disk(self, filepath):
        with open(filepath, "wb") as f:
            pickle.dump(self, f)

    @staticmethod
    def load_from_disk(filepath) -> "DataProto":
        with open(filepath, "rb") as f:
            data = pickle.load(f)
            return data

    def print_size(self, prefix=""):
        size_of_tensordict = 0
        for key, tensor in self.batch.items():
            size_of_tensordict += tensor.element_size() * tensor.numel()
        size_of_numpy_array = 0
        for key, numpy_array in self.non_tensor_batch.items():
            size_of_numpy_array += numpy_array.nbytes

        size_of_numpy_array /= 1024**3
        size_of_tensordict /= 1024**3

        message = f"Size of tensordict: {size_of_tensordict} GB, size of non_tensor_batch: {size_of_numpy_array} GB"

        if prefix:
            message = f"{prefix}, " + message
        print(message)

    def check_consistency(self):
        """Check the consistency of the DataProto. Mainly for batch and non_tensor_batch

        We expose this function as a public one so that user can call themselves directly

        """
        if self.batch is not None:
            assert len(self.batch.batch_size) == 1, "only support num_batch_dims=1"

        if self.non_tensor_batch is not None:
            for key, val in self.non_tensor_batch.items():
                assert isinstance(val, np.ndarray)

        if self.batch is not None and len(self.non_tensor_batch) != 0:
            # TODO: we can actually lift this restriction if needed
            assert len(self.batch.batch_size) == 1, "only support num_batch_dims=1 when non_tensor_batch is not empty."

            batch_size = self.batch.batch_size[0]
            for key, val in self.non_tensor_batch.items():
                assert isinstance(val, np.ndarray), f"data in the non_tensor_batch must be a numpy.array with dtype=object, but for {key=}, got {type(val)=}"
                assert val.shape[0] == batch_size, f"key {key} length {len(val)} is not equal to batch size {batch_size}"

    @classmethod
    def from_single_dict(cls, data: Dict[str, Union[torch.Tensor, np.ndarray]], meta_info=None, auto_padding=False):
        """Create a DataProto from a dict of tensors and non_tensors"""
        tensors = {}
        non_tensors = {}

        for key, val in data.items():
            if isinstance(val, torch.Tensor):
                tensors[key] = val
            elif isinstance(val, np.ndarray):
                non_tensors[key] = val
            else:
                raise ValueError(f"Unsupported type in data {type(val)}")

        return cls.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info, auto_padding=auto_padding)

    @classmethod
    def from_dict(cls, tensors: Dict[str, torch.Tensor], non_tensors=None, meta_info=None, num_batch_dims=1, auto_padding=False):
        """Create a DataProto from a dict of tensors. This assumes that

        1. All the tensor in tensors have the same dim0

        2. Only dim0 is the batch dim

        """
        assert len(tensors) > 0, "tensors must not be empty"
        assert num_batch_dims > 0, "num_batch_dims must be greater than zero"
        if non_tensors is not None:
            assert num_batch_dims == 1, "only support num_batch_dims=1 when non_tensors is not None."

        if meta_info is None:
            meta_info = {}
        if non_tensors is None:
            non_tensors = {}

        assert isinstance(non_tensors, dict)

        # get and check batch size
        batch_size = None
        pivot_key = None
        for key, tensor in tensors.items():
            if batch_size is None:
                batch_size = tensor.shape[:num_batch_dims]
                pivot_key = key
            else:
                current_batch = tensor.shape[:num_batch_dims]
                assert batch_size == current_batch, f"Not all the tensor in tensors have the same batch size with batch_dims={num_batch_dims}. Got {pivot_key} has {batch_size}, {key} has {current_batch}"

        for key, val in non_tensors.items():
            non_tensors[key] = np.array(val, dtype=object)

        tensor_dict = TensorDict(source=tensors, batch_size=batch_size)
        if auto_padding:
            meta_info[DataProtoConfig.auto_padding_key] = True
        return cls(batch=tensor_dict, non_tensor_batch=non_tensors, meta_info=meta_info)

    def to(self, device) -> "DataProto":
        """move the batch to device



        Args:

            device (torch.device, str): torch device



        Returns:

            DataProto: the current DataProto



        """
        if self.batch is not None:
            self.batch = self.batch.to(device)
        return self

    def select(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_keys=None, deepcopy=False) -> "DataProto":
        """Select a subset of the DataProto via batch_keys and meta_info_keys



        Args:

            batch_keys (list, optional): a list of strings indicating the keys in batch to select

            meta_info_keys (list, optional): a list of keys indicating the meta info to select



        Returns:

            DataProto: the DataProto with the selected batch_keys and meta_info_keys

        """
        # TODO (zhangchi.usc1992) whether to copy
        if batch_keys is not None:
            batch_keys = tuple(batch_keys)
            sub_batch = self.batch.select(*batch_keys)
        else:
            sub_batch = self.batch

        if non_tensor_batch_keys is not None:
            non_tensor_batch = {key: val for key, val in self.non_tensor_batch.items() if key in non_tensor_batch_keys}
        else:
            non_tensor_batch = self.non_tensor_batch

        if deepcopy:
            non_tensor_batch = copy.deepcopy(non_tensor_batch)

        if meta_info_keys is not None:
            sub_meta_info = {key: val for key, val in self.meta_info.items() if key in meta_info_keys}
        else:
            sub_meta_info = self.meta_info

        if deepcopy:
            sub_meta_info = copy.deepcopy(sub_meta_info)

        return type(self)(batch=sub_batch, non_tensor_batch=non_tensor_batch, meta_info=sub_meta_info)

    def select_idxs(self, idxs):
        """

        Select specific indices from the DataProto.



        Args:

            idxs (torch.Tensor or numpy.ndarray or list): Indices to select



        Returns:

            DataProto: A new DataProto containing only the selected indices

        """
        if isinstance(idxs, list):
            idxs = torch.tensor(idxs)
            if idxs.dtype != torch.bool:
                idxs = idxs.type(torch.int32)

        if isinstance(idxs, np.ndarray):
            idxs_np = idxs
            idxs_torch = torch.from_numpy(idxs)
        else:  # torch.Tensor
            idxs_torch = idxs
            idxs_np = idxs.detach().cpu().numpy()

        batch_size = idxs_np.sum() if idxs_np.dtype == bool else idxs_np.shape[0]

        if self.batch is not None:
            # Use TensorDict's built-in indexing capabilities
            selected_batch = TensorDict(source={key: tensor[idxs_torch] for key, tensor in self.batch.items()}, batch_size=(batch_size,))
        else:
            selected_batch = None

        selected_non_tensor = {}
        for key, val in self.non_tensor_batch.items():
            selected_non_tensor[key] = val[idxs_np]

        return type(self)(batch=selected_batch, non_tensor_batch=selected_non_tensor, meta_info=self.meta_info)

    def slice(self, start=None, end=None, step=None):
        """

        Slice the DataProto and return a new DataProto object.

        This is an improved version of direct slicing which returns a DataProtoItem.



        Args:

            start (int, optional): Start index. Defaults to None (start from beginning).

            end (int, optional): End index (exclusive). Defaults to None (go to end).

            step (int, optional): Step size. Defaults to None (step=1).



        Returns:

            DataProto: A new DataProto containing the sliced data



        Examples:

            # Using the slice method directly

            sliced_data = data_proto.slice(10, 20)



            # Using enhanced indexing (returns DataProto)

            sliced_data = data_proto[10:20]

            sliced_data = data_proto[::2]  # Every other element



            # Using list indexing (returns DataProto)

            indices = [1, 5, 10]

            selected_data = data_proto[indices]



            # Single index still returns DataProtoItem

            single_item = data_proto[5]

        """
        # Create a slice object
        slice_obj = slice(start, end, step)

        # Handle the batch data
        if self.batch is not None:
            # Use TensorDict's built-in slicing capabilities
            sliced_batch = self.batch[slice_obj]
        else:
            sliced_batch = None

        # Handle the non-tensor batch data
        sliced_non_tensor = {}
        for key, val in self.non_tensor_batch.items():
            sliced_non_tensor[key] = val[slice_obj]

        # Return a new DataProto object
        return type(self)(batch=sliced_batch, non_tensor_batch=sliced_non_tensor, meta_info=self.meta_info)

    def pop(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_keys=None) -> "DataProto":
        """Pop a subset of the DataProto via `batch_keys` and `meta_info_keys`



        Args:

            batch_keys (list, optional): a list of strings indicating the keys in batch to pop

            meta_info_keys (list, optional): a list of keys indicating the meta info to pop



        Returns:

            DataProto: the DataProto with the poped batch_keys and meta_info_keys

        """
        assert batch_keys is not None
        if meta_info_keys is None:
            meta_info_keys = []
        if non_tensor_batch_keys is None:
            non_tensor_batch_keys = []

        tensors = {}
        # tensor batch
        for key in batch_keys:
            assert key in self.batch.keys()
            tensors[key] = self.batch.pop(key)
        non_tensors = {}
        # non tensor batch
        for key in non_tensor_batch_keys:
            assert key in self.non_tensor_batch.keys()
            non_tensors[key] = self.non_tensor_batch.pop(key)
        meta_info = {}
        for key in meta_info_keys:
            assert key in self.meta_info.keys()
            meta_info[key] = self.meta_info.pop(key)
        return DataProto.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info)

    def rename(self, old_keys=None, new_keys=None) -> "DataProto":
        """

        Note that this function only rename the key in the batch

        """

        def validate_input(keys):
            if keys is not None:
                if isinstance(keys, str):
                    keys = [keys]
                elif isinstance(keys, list):
                    pass
                else:
                    raise TypeError(f"keys must be a list or a string, but got {type(keys)}")
            return keys

        old_keys = validate_input(old_keys)
        new_keys = validate_input(new_keys)

        if len(new_keys) != len(old_keys):
            raise ValueError(f"new_keys and old_keys must have the same length, but got {len(new_keys)} and {len(old_keys)}")

        self.batch.rename_key_(tuple(old_keys), tuple(new_keys))

        return self

    def union(self, other: "DataProto") -> "DataProto":
        """Union with another DataProto. Union batch and meta_info separately.

        Throw an error if



        - there are conflict keys in batch and they are not equal

        - the batch size of two data batch is not the same

        - there are conflict keys in meta_info and they are not the same.



        Args:

            other (DataProto): another DataProto to union



        Returns:

            DataProto: the DataProto after union

        """
        self.batch = union_tensor_dict(self.batch, other.batch)
        self.non_tensor_batch = union_numpy_dict(self.non_tensor_batch, other.non_tensor_batch)
        self.meta_info = union_two_dict(self.meta_info, other.meta_info)
        return self

    def make_iterator(self, mini_batch_size, epochs, seed=None, dataloader_kwargs=None):
        r"""Make an iterator from the DataProto. This is built upon that TensorDict can be used as a normal Pytorch

        dataset. See https://pytorch.org/tensordict/tutorials/data_fashion for more details.





        Args:

            mini_batch_size (int): mini-batch size when iterating the dataset. We require that ``batch.batch_size[0] % mini_batch_size == 0``.

            epochs (int): number of epochs when iterating the dataset.

            dataloader_kwargs (Any): internally, it returns a DataLoader over the batch. The dataloader_kwargs is the kwargs passed to the DataLoader.



        Returns:

            Iterator: an iterator that yields a mini-batch data at a time. The total number of iteration steps is ``self.batch.batch_size * epochs // mini_batch_size``

        """
        assert self.batch.batch_size[0] % mini_batch_size == 0, f"{self.batch.batch_size[0]} % {mini_batch_size} != 0"
        # we can directly create a dataloader from TensorDict
        if dataloader_kwargs is None:
            dataloader_kwargs = {}

        if seed is not None:
            generator = torch.Generator()
            generator.manual_seed(seed)
        else:
            generator = None

        assert isinstance(dataloader_kwargs, Dict)
        train_dataloader = DataLoader(dataset=self, batch_size=mini_batch_size, collate_fn=collate_fn, generator=generator, **dataloader_kwargs)

        def get_data():
            for _ in range(epochs):
                for d in train_dataloader:
                    d.meta_info = self.meta_info
                    yield d

        return iter(get_data())

    def is_padding_enabled(self):
        """

        Check if padding is enabled for the DataProto.

        Returns:

            bool: True if padding is enabled, False otherwise.

        """
        dataproto_specific_padding = self.meta_info.get(DataProtoConfig.auto_padding_key, False)
        return dataproto_specific_padding or DataProtoConfig.auto_padding

    def padding(self, padding_size, padding_candidate=""):
        """Pad the DataProto by concating with padding_candidate.repeat(padding_size)



        Args:

            padding_size (int): the number of repeated padding_candidate

            padding_candidate: the item to be repeated and appended to the DataProto, only supporting ["first", "last"]

        """
        if padding_size == 0:
            return
        padding_candidate = self.select_idxs([0 if padding_candidate == "first" else len(self) - 1])
        padding_part = padding_candidate.repeat(padding_size)
        padded_dp = DataProto.concat([self, padding_part])
        self.batch = padded_dp.batch
        self.non_tensor_batch = padded_dp.non_tensor_batch

    def chunk(self, chunks: int) -> List["DataProto"]:
        """Split the batch among dim=0 into chunks. The meta_info is passed to each DataProto after split.



        Args:

            chunks (int): the number of chunks to split on dim=0



        Returns:

            List[DataProto]: a list of DataProto after splitting

        """
        if not self.is_padding_enabled():
            assert len(self) % chunks == 0, f"only support equal chunk. Got size of DataProto {len(self)} and chunk {chunks}."

        bsz_in_batch = None
        if self.batch is not None:
            batch_lst = self.batch.chunk(chunks=chunks, dim=0)
            bsz_in_batch = np.array([batch.batch_size[0] for batch in batch_lst])
            chunk_indices = np.cumsum(bsz_in_batch)[:-1]
        else:
            batch_lst = [None for _ in range(chunks)]

        non_tensor_batch_lst = [{} for _ in range(chunks)]
        for key, val in self.non_tensor_batch.items():
            assert isinstance(val, np.ndarray)
            if bsz_in_batch is not None:
                non_tensor_lst = np.array_split(val, chunk_indices.tolist())
            else:
                non_tensor_lst = np.array_split(val, chunks)
            assert len(non_tensor_lst) == chunks
            for i in range(chunks):
                non_tensor_batch_lst[i][key] = non_tensor_lst[i]

        output = []
        for i in range(chunks):
            output.append(type(self)(batch=batch_lst[i], non_tensor_batch=non_tensor_batch_lst[i], meta_info=self.meta_info))

        return output

    @staticmethod
    def concat(data: List["DataProto"]) -> "DataProto":
        """Concat a list of DataProto. The batch is concatenated among dim=0.

        The meta_info is assumed to be identical and will use the first one.



        Args:

            data (List[DataProto]): list of DataProto



        Returns:

            DataProto: concatenated DataProto

        """
        batch_lst = []
        for batch in data:
            batch_lst.append(batch.batch)
        new_batch = torch.cat(batch_lst, dim=0) if batch_lst[0] is not None else None

        non_tensor_batch = list_of_dict_to_dict_of_list(list_of_dict=[d.non_tensor_batch for d in data])
        for key, val in non_tensor_batch.items():
            non_tensor_batch[key] = np.concatenate(val, axis=0)

        cls = type(data[0]) if len(data) > 0 else DataProto
        return cls(batch=new_batch, non_tensor_batch=non_tensor_batch, meta_info=data[0].meta_info)

    def reorder(self, indices):
        """

        Note that this operation is in-place

        """
        indices_np = indices.detach().numpy()
        self.batch = self.batch[indices]
        self.non_tensor_batch = {key: val[indices_np] for key, val in self.non_tensor_batch.items()}

    def repeat(self, repeat_times=2, interleave=True):
        """

        Repeat the batch data a specified number of times.



        Args:

            repeat_times (int): Number of times to repeat the data.

            interleave (bool): Whether to interleave the repeated data.



        Returns:

            DataProto: A new DataProto with repeated data.

        """
        if self.batch is not None:
            if interleave:
                # Interleave the data
                repeated_tensors = {key: tensor.repeat_interleave(repeat_times, dim=0) for key, tensor in self.batch.items()}
            else:
                # Stack the data
                repeated_tensors = {key: tensor.unsqueeze(0).expand(repeat_times, *tensor.shape).reshape(-1, *tensor.shape[1:]) for key, tensor in self.batch.items()}

            repeated_batch = TensorDict(
                source=repeated_tensors,
                batch_size=(self.batch.batch_size[0] * repeat_times,),
            )
        else:
            repeated_batch = None

        repeated_non_tensor_batch = {}
        for key, val in self.non_tensor_batch.items():
            if interleave:
                repeated_non_tensor_batch[key] = np.repeat(val, repeat_times, axis=0)
            else:
                repeated_non_tensor_batch[key] = np.tile(val, (repeat_times,) + (1,) * (val.ndim - 1))

        return type(self)(
            batch=repeated_batch,
            non_tensor_batch=repeated_non_tensor_batch,
            meta_info=self.meta_info,
        )


@dataclass
class DataProtoFuture:
    """

    DataProtoFuture aims to eliminate actual data fetching on driver. By doing so, the driver doesn't have to wait

    for data so that asynchronous execution becomes possible.

    DataProtoFuture contains a list of futures from another WorkerGroup of size world_size.

    - collect_fn is a Callable that reduces the list of futures to a DataProto

    - dispatch_fn is a Callable that partitions the DataProto into a list of DataProto of size world_size and then select



    Potential issue: we can optimize dispatch_fn(collect_fn) such that only needed data is fetched on destination

    - DataProtoFuture only supports directly passing from the output of a method to another input. You can't perform any

    operation on the DataProtoFuture in driver.

    """

    collect_fn: Callable
    futures: List[ray.ObjectRef]
    dispatch_fn: Callable = None

    @staticmethod
    def concat(data: List[ray.ObjectRef]) -> "DataProtoFuture":
        output = DataProtoFuture(collect_fn=DataProto.concat, futures=data)
        return output

    def chunk(self, chunks: int) -> List["DataProtoFuture"]:
        from functools import partial

        arg_future_lst = []
        for i in range(chunks):
            # note that we can't directly pass i and chunks
            def dispatch_fn(x, i, chunks):
                return x.chunk(chunks=chunks)[i]

            arg_future = DataProtoFuture(collect_fn=self.collect_fn, dispatch_fn=partial(dispatch_fn, i=i, chunks=chunks), futures=self.futures)
            arg_future_lst.append(arg_future)
        return arg_future_lst

    def get(self):
        output = ray.get(self.futures)  # dp_size.
        for o in output:
            assert isinstance(o, DataProto)
        output = self.collect_fn(output)  # select dp, concat
        if self.dispatch_fn is not None:
            output = self.dispatch_fn(output)  # split in batch dim, select using dp
        return output


def all_gather_data_proto(data: DataProto, process_group):
    # Note that this is an inplace operator just like torch.distributed.all_gather
    group_size = torch.distributed.get_world_size(group=process_group)
    assert isinstance(data, DataProto)
    prev_device = data.batch.device
    data.batch = data.batch.cuda(device=torch.cuda.current_device())
    data.batch = allgather_dict_tensors(data.batch.contiguous(), size=group_size, group=process_group, dim=0)
    data.batch = data.batch.to(prev_device)
    # all gather non_tensor_batch
    all_non_tensor_batch = [None for _ in range(group_size)]
    torch.distributed.all_gather_object(all_non_tensor_batch, data.non_tensor_batch, group=process_group)
    data.non_tensor_batch = {k: np.concatenate([d[k] for d in all_non_tensor_batch]) for k in data.non_tensor_batch}