Import FastNLP fails for versions 1.0.1

Describe the bug Import FastNLP fails for versions 1.0.1

To Reproduce Steps to reproduce the behavior: Python 3.11, $ pip install FastNLP>=1.0.0alpha

import FastNLP

Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[1], line 13
---> 13 from fastNLP import DataSet

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/__init__.py:3
      2 from fastNLP.envs import *
----> 3 from fastNLP.core import *
      5 __version__ = '1.0.1'

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/__init__.py:114
      1 __all__ = [
      2     # callbacks
      3     'Callback',
   (...)
    112     'Vocabulary'
    113 ]
--> 114 from .callbacks import *
    115 from .collators import *
    116 from .controllers import *

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/callbacks/__init__.py:41
     39 from .load_best_model_callback import LoadBestModelCallback
     40 from .early_stop_callback import EarlyStopCallback
---> 41 from .torch_callbacks import *
     42 from .more_evaluate_callback import MoreEvaluateCallback
     43 from .has_monitor_callback import ResultsMonitor, HasMonitorCallback

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/callbacks/torch_callbacks/__init__.py:8
      1 __all__ = [
      2     'TorchWarmupCallback',
      3     'TorchGradClipCallback'
      4 ]
      7 from .torch_lr_sched_callback import TorchWarmupCallback
----> 8 from .torch_grad_clip_callback import TorchGradClipCallback

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/callbacks/torch_callbacks/torch_grad_clip_callback.py:6
      4 from typing import Union, List
      5 from ..callback import Callback
----> 6 from ...drivers.torch_driver.fairscale import FairScaleDriver
      7 from ...drivers.torch_driver import TorchDriver
      8 from fastNLP.envs.imports import _NEED_IMPORT_FAIRSCALE

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/drivers/__init__.py:30
      1 __all__ = [
      2     'Driver',
      3     'TorchDriver',
   (...)
     27     'optimizer_state_to_device'
     28 ]
---> 30 from .torch_driver import TorchDriver, TorchSingleDriver, TorchDDPDriver, DeepSpeedDriver, FairScaleDriver, \
     31     TorchFSDPDriver, torch_seed_everything, optimizer_state_to_device
     32 from .jittor_driver import JittorDriver, JittorMPIDriver, JittorSingleDriver
     33 from .paddle_driver import PaddleDriver, PaddleFleetDriver, PaddleSingleDriver, paddle_seed_everything

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/drivers/torch_driver/__init__.py:18
     16 from .torch_driver import TorchDriver
     17 from .deepspeed import DeepSpeedDriver
---> 18 from .torch_fsdp import TorchFSDPDriver
     19 from .utils import torch_seed_everything, optimizer_state_to_device

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/fastNLP/core/drivers/torch_driver/torch_fsdp.py:7
      4 from fastNLP.envs.imports import _TORCH_GREATER_EQUAL_1_12, _NEED_IMPORT_TORCH
      6 if _TORCH_GREATER_EQUAL_1_12:
----> 7     from torch.distributed.fsdp import FullyShardedDataParallel, StateDictType, FullStateDictConfig, OptimStateKeyType
      9 if _NEED_IMPORT_TORCH:
     10     import torch

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/fsdp/__init__.py:1
----> 1 from .flat_param import FlatParameter
      2 from .fully_sharded_data_parallel import (
      3     BackwardPrefetch,
      4     CPUOffload,
   (...)
     11     StateDictType,
     12 )
     13 from .wrap import ParamExecOrderWrapPolicy

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/fsdp/flat_param.py:26
     23 import torch.nn.functional as F
     24 from torch import Tensor
---> 26 from ._fsdp_extensions import _ext_post_unflatten_transform, _ext_pre_flatten_transform
     27 from ._utils import _alloc_storage, _free_storage, _set_fsdp_flattened, p_assert
     29 __all__ = [
     30     "FlatParameter",
     31     "FlatParamHandle",
   (...)
     37     "HandleTrainingState",
     38 ]

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/fsdp/_fsdp_extensions.py:7
      4 import torch
      5 import torch.distributed as dist
----> 7 from torch.distributed.fsdp._shard_utils import _create_chunk_sharded_tensor
     10 class FSDPExtensions(ABC):
     11     """
     12     This enables some customizable hooks to enable composability with tensor
     13     parallelism. To activate these hooks, use :func:`_set_fsdp_extensions` to
     14     set a custom :class:`FSDPExtensions` that implements the hooks.
     15     """

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/fsdp/_shard_utils.py:10
      8 import torch.nn.functional as F
      9 from torch.distributed import distributed_c10d
---> 10 from torch.distributed._shard.sharded_tensor import (
     11     Shard,
     12     ShardedTensor,
     13     ShardedTensorMetadata,
     14     TensorProperties,
     15 )
     16 from torch.distributed._shard.sharding_spec import (
     17     ChunkShardingSpec,
     18     EnumerableShardingSpec,
     19     ShardingSpec,
     20     ShardMetadata,
     21 )
     24 def _sharding_spec_to_offsets(
     25     sharding_spec: ShardingSpec, tensor_numel: int, world_size: int
     26 ) -> List[int]:

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/_shard/__init__.py:1
----> 1 from .api import (
      2     _replicate_tensor,
      3     _shard_tensor,
      4     load_with_process_group,
      5     shard_module,
      6     shard_parameter,
      7 )

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/_shard/api.py:6
      4 import torch.nn as nn
      5 from torch.distributed import distributed_c10d
----> 6 from torch.distributed._shard.sharded_tensor import (
      7     ShardedTensor,
      8     _PartialTensor
      9 )
     10 from .replicated_tensor import ReplicatedTensor
     11 from .sharding_spec import (
     12     ShardingSpec,
     13     ChunkShardingSpec
     14 )

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py:8
      5 from typing import List
      7 import torch
----> 8 import torch.distributed._shard.sharding_spec as shard_spec
      9 from torch.distributed._shard.partial_tensor import _PartialTensor
     11 from .api import (
     12     _CUSTOM_SHARDED_OPS,
     13     _SHARDED_OPS,
   (...)
     18     TensorProperties,
     19 )

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/_shard/sharding_spec/__init__.py:1
----> 1 from .api import (
      2     DevicePlacementSpec,
      3     EnumerableShardingSpec,
      4     PlacementSpec,
      5     ShardingSpec,
      6     _infer_sharding_spec_from_shards_metadata,
      7 )
      8 from .chunk_sharding_spec import (
      9     ChunkShardingSpec,
     10 )
     12 from torch.distributed._shard.metadata import ShardMetadata

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/_shard/sharding_spec/api.py:16
      8 from ._internals import (
      9     check_tensor,
     10     get_chunked_dim_size,
     11     get_split_size,
     12     validate_non_overlapping_shards_metadata
     13 )
     14 from torch.distributed._shard.metadata import ShardMetadata
---> 16 import torch.distributed._shard.sharded_tensor.metadata as sharded_tensor_meta
     17 from torch.distributed._shard.op_registry_utils import _decorator_func
     19 if TYPE_CHECKING:
     20     # Only include ShardedTensor when do type checking, exclude it
     21     # from run-time to resolve circular dependency.

File ~/condaenvs/bbt-hf425/lib/python3.11/site-packages/torch/distributed/_shard/sharded_tensor/metadata.py:70
     61     @staticmethod
     62     def create_from_tensor(tensor: torch.Tensor) -> "TensorProperties":
     63         return TensorProperties(
     64             dtype=tensor.dtype,
     65             layout=tensor.layout,
   (...)
     68             pin_memory=tensor.is_pinned()
     69         )
---> 70 @dataclass
     71 class ShardedTensorMetadata(object):
     72     """
     73     Represents metadata for :class:`ShardedTensor`
     74     """
     76     # Metadata about each shard of the Tensor

File ~/condaenvs/bbt-hf425/lib/python3.11/dataclasses.py:1221, in dataclass(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, weakref_slot)
   1218     return wrap
   1220 # We're called as @dataclass without parens.
-> 1221 return wrap(cls)

File ~/condaenvs/bbt-hf425/lib/python3.11/dataclasses.py:1211, in dataclass.<locals>.wrap(cls)
   1210 def wrap(cls):
-> 1211     return _process_class(cls, init, repr, eq, order, unsafe_hash,
   1212                           frozen, match_args, kw_only, slots,
   1213                           weakref_slot)

File ~/condaenvs/bbt-hf425/lib/python3.11/dataclasses.py:959, in _process_class(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, weakref_slot)
    956         kw_only = True
    957     else:
    958         # Otherwise it's a field of some type.
--> 959         cls_fields.append(_get_field(cls, name, type, kw_only))
    961 for f in cls_fields:
    962     fields[f.name] = f

File ~/condaenvs/bbt-hf425/lib/python3.11/dataclasses.py:816, in _get_field(cls, a_name, a_type, default_kw_only)
    812 # For real fields, disallow mutable defaults.  Use unhashable as a proxy
    813 # indicator for mutability.  Read the __hash__ attribute from the class,
    814 # not the instance.
    815 if f._field_type is _FIELD and f.default.__class__.__hash__ is None:
--> 816     raise ValueError(f'mutable default {type(f.default)} for field '
    817                      f'{f.name} is not allowed: use default_factory')
    819 return f

ValueError: mutable default <class 'torch.distributed._shard.sharded_tensor.metadata.TensorProperties'> for field tensor_properties is not allowed: use default_factory

fastnlp / fastNLP

Import FastNLP fails for versions 1.0.1 #448