dataclasses-tensor 0.2.0

Easily serialize dataclasses to and from tensors

Dataclasses Tensor

The library provides a simple API for encoding and decoding Python dataclasses to and from tensors (PyTorch, TensorFlow, or NumPy arrays) based on typing annotations.

Heavily inspired by dataclasses-json package.

Install

pip install dataclasses-tensor

Quickstart

Tensor representation for a game state in Chess:

from dataclasses import dataclass, field
from dataclasses_tensor import dataclass_tensor, config
from enum import Enum
from typing import Optional, List

class Player(Enum):
  WHITE = 0
  BLACK = 1

class PieceType(Enum):
  PAWN = 0
  BISHOP = 1
  KNIGHT = 2
  ROOK = 3
  QUEEN = 4
  KING = 5

@dataclass
class Piece:
  piece_type: PieceType
  owner: Player

@dataclass_tensor
@dataclass
class Chess:
  num_moves: float
  next_move: Player
  board: List[Optional[Piece]] = field(metadata=config(shape=(64,)))

Working with tensors:

>>> state = Chess(100., next_move=Player.WHITE, board=[Piece(PieceType.KING, Player.BLACK)])
>>> t1 = state.to_numpy()
>>> t1.shape
(579,)
>>> Chess.from_numpy(t1)
Chess(num_moves=100., next_move=<Player.WHITE: 0>, board=[Piece(piece_type=<PieceType.KING: 5>, owner=<Player.BLACK: 1>), ...])

Types

Data Classes

The library uses type annotations to determine appropriate encoding layout. Data class member variables serialized sequentially. See supported types listed below.

Primitives (int, float, bool)

The library supports numerical primitives (int, float) and bool. Strings and byte arrays are not supported.

Warning: be careful with tensor dtype as an implicit type conversion could potentially lead to losing information (for example, writing float into int32 tensor and reading it back won't produce expected result).

Enums

Python Enums are encoded using one-hot encoding.

>>> from dataclasses_tensor import dataclass_tensor
>>> from dataclasses import dataclass
>>> from enum import Enum
>>>
>>> class Matrix(Enum):
...     THE_MATRIX = 1
...     RELOADED = 2
...     REVOLUTIONS = 3
...
>>> @dataclass_tensor
... @dataclass
... class WatchList:
...     matrix: Matrix
...
>>> WatchList(Matrix.RELOADED).to_numpy()
array([0., 0., 1.])
>>> WatchList.from_numpy(_)
WatchList(matrix=<Matrix.RELOADED: 2>)

Optional

typing.Optional type is encoded using additional dimension prior to the main datatype.

>>> from typing import Optional
>>>
>>> @dataclass_tensor
... @dataclass
... class MaybeWatchList:
...     matrix: Optional[Matrix]
>>>
>>> MaybeWatchList(Matrix.RELOADED).to_numpy()
array([0., 0., 1., 0.])
>>> MaybeWatchList.from_numpy([0., 0., 1., 0.])
MaybeWatchList(matrix=<Matrix.RELOADED: 2>)
>>> MaybeWatchList.from_numpy([1., 0., 0., 0.])
MaybeWatchList(matrix=None)

The layout described for Optional[Enum] is consistent with having None as additional option into enumeration.

Arrays

Arrays, defined either using typing.List or [] (supported in Python3.9+), require size to be statically provided. See example:

>>> from typing import List
>>> from dataclasses_tensor import config

>>> @dataclass_tensor
... @dataclass
... class MultipleWatchList:
...     matrices: List[Matrix] = field(metadata=config(shape=(2,)))
>>>
>>> MultipleWatchList([Matrix.THE_MATRIX, Matrix.RELOADED]).to_numpy()
array([1., 0., 0., 0., 1., 0.])
>>> MultipleWatchList.from_numpy([1., 0., 0., 0., 1., 0.])
MultipleWatchList(matrices=[<Matrix.THE_MATRIX: 1>, <Matrix.RELOADED: 2>])

Nested lists are supported, note multidimensional shape configuration:

>>> @dataclass_tensor
... @dataclass
... class MultipleWatchList:
...     matrices: List[List[Matrix]] = field(metadata=config(shape=(1,2)))
>>>
>>> MultipleWatchList([[Matrix.THE_MATRIX, Matrix.RELOADED]]).to_numpy()
array([1., 0., 0., 0., 1., 0.])
>>> MultipleWatchList.from_numpy([1., 0., 0., 0., 1., 0.])
MultipleWatchList(matrices=[[<Matrix.THE_MATRIX: 1>, <Matrix.RELOADED: 2>]])

If List argument is Optional, the list is automatically padded to the right shape with Nones.

>>> @dataclass_tensor
... @dataclass
... class MaybeMultipleWatchList:
...     matrices: List[Optional[Matrix]] = field(metadata=config(shape=(3,)))
>>>
>>> MaybeMultipleWatchList([Matrix.THE_MATRIX, Matrix.RELOADED]).to_numpy()
array([0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.])
>>> MaybeMultipleWatchList.from_numpy([0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.])
MaybeMultipleWatchList(matrices=[<Matrix.THE_MATRIX: 1>, <Matrix.RELOADED: 2>, None])

Union

typing.Union is encoded by allocating one-hot tensor to determine which option from the union is given following by corresponding layouts for all options.

>>> from typing import Union
>>>
>>> class Batman(Enum):
...     BEGINS = 1
...     DARK_KNIGHT = 2
...     DARK_KINGHT_RISES = 3
...
>>> @dataclass_tensor
... @dataclass
... class WatchList:
...     next_movie: Union[Matrix, Batman]
...
>>> WatchList(Matrix.RELOADED).to_numpy()
array([1., 0., 0., 1., 0., 0., 0., 0.])
>>> WatchList.from_numpy(_)
WatchList(next_movie=<Matrix.RELOADED: 2>)
>>> WatchList(Batman.DARK_KNIGHT).to_numpy()
array([0., 1., 0., 0., 0., 0., 1., 0.])
>>> WatchList.from_numpy(_)
WatchList(next_movie=<Batman.DARK_KNIGHT: 2>)

Decoding is a fairly straigtforward process though encoding might be somewhat problematic: Python's typing is not designed to provide separation-by-construction for union types. The library uses simple isinstance checks to test out all types provided against a given value, first match is used. The library does not traverse generics, origins, supertypes, etc. So, be diligent defining of Union.

Recursive Definitions

Recursive definitions, like linked lists, trees, graphs etc, are not supported. From a usability and performance point of view, it's crucial for encoder/decoder to be able to evaluate statically output tensor size.

Targets

The library supports the following containers as tensors:

• NumPy ndarray with to_numpy/from_numpy
• PyTorch tensors with to_torch/from_torch
• TensorFlow tensors with to_tf/from_tf

Note, that dependencies are not installed with the library itself (TensorFlow, PyTorch or NumPy) and should be provided at runtime.

Performance

Tensor layout is not cached and is computed for each operation. When performing a lot of operations with class definition staying the same, it makes sense to re-use layout. For example:

>>> class Matrix(Enum):
...     THE_MATRIX = 1
...     RELOADED = 2
...     REVOLUTIONS = 3
...
>>> @dataclass_tensor
... @dataclass
... class WatchList:
...     matrix: Matrix
...
>>> layout = WatchList.tensor_layout()
>>> WatchList(Matrix.RELOADED).to_numpy(tensor_layout=layout)
array([0., 0., 1.])
>>> WatchList.from_numpy(_, tensor_layout=layout)
WatchList(matrix=<Matrix.RELOADED: 2>)

Advanced Features

Dtype

The library supports float and integer (long) tensors. The data type could be specified either as a parameter to the dataclass_tensor decorator (applied to all operations) or independently as an argument to to_tensor function call. See examples below.

dtype argument is passed to the corresponding target library, e.g. NumPy (docs), PyTorch (docs) or TensorFlow.

>>> class Matrix(Enum):
...     THE_MATRIX = 1
...     RELOADED = 2
...     REVOLUTIONS = 3
...
>>> @dataclass_tensor
... @dataclass
... class WatchList:
...     matrix: Matrix
...
>>> WatchList(Matrix.RELOADED).to_numpy()
array([0., 0., 1.], dtype=float32)
>>> WatchList(Matrix.RELOADED).to_numpy(dtype="int32")
array([0, 0, 1], dtype=int32)

or with defaults setup in a decorator

>>> class Matrix(Enum):
...     THE_MATRIX = 1
...     RELOADED = 2
...     REVOLUTIONS = 3
...
>>> @dataclass_tensor(dtype="int32")
... @dataclass
... class WatchList:
...     matrix: Matrix
...
>>> WatchList(Matrix.RELOADED).to_numpy()
array([0, 0, 1], dtype=int32)

Custom Attribute Resolver

TBD

Batch

TBD

TODO

• Tests suite for dtypes
• Tests suite for PyTorch and TensorFlow adapters
• Custom attribute resolver (e.g. from dict instead of class instance)
• Batch operations (write many/read many)
• Pretty-print for tensor layout object

Contributing

• Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
• Fork the repository on Github & branch from main to feature-* to start making your changes.
• Write a test which shows that the bug was fixed or that the feature works as expected.

or simply...

• Use it.
• Enjoy it.
• Spread the word.

License

Copyright © 2021, Oleksii Kachaiev.

dataclasses-tensor is licensed under the MIT license, available at MIT and also in the LICENSE file.