Metadata-Version: 2.4
Name: batchtensor
Version: 0.1.1
Summary: Functions to manipulate batches of PyTorch tensors
License: BSD-3-Clause
License-File: LICENSE
Keywords: batch,sequence,pytorch,tensor
Author: Thibaut Durand
Author-email: durand.tibo+gh@gmail.com
Requires-Python: >=3.10,<3.15
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Provides-Extra: all
Requires-Dist: coola (>=0.9.1,<1.0)
Requires-Dist: numpy (>=1.24,<3.0) ; extra == "all"
Requires-Dist: numpy (>=2.1,<3.0) ; (python_version == "3.13") and (extra == "all")
Requires-Dist: torch (>=2.4,<3.0)
Project-URL: Homepage, https://github.com/durandtibo/batchtensor
Project-URL: Repository, https://github.com/durandtibo/batchtensor
Description-Content-Type: text/markdown

# batchtensor

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## Overview

`batchtensor` is lightweight library built on top of [PyTorch](https://pytorch.org/) to manipulate
nested data structure with PyTorch tensors.
This library provides functions for tensors where the first dimension is the batch dimension.
It also provides functions for tensors representing a batch of sequences where the first dimension
is the batch dimension and the second dimension is the sequence dimension.

- [Motivation](#motivation)
- [Documentation](https://durandtibo.github.io/batchtensor/)
- [Installation](#installation)
- [Contributing](#contributing)
- [API stability](#api-stability)
- [License](#license)

## Motivation

Let's imagine you have a batch which is represented by a dictionary with three tensors, and you want
to take the first 2 items.
`batchtensor` provides the function `slice_along_batch` that allows to slide all the tensors:

```pycon
>>> import torch
>>> from batchtensor.nested import slice_along_batch
>>> batch = {
...     "a": torch.tensor([[2, 6], [0, 3], [4, 9], [8, 1], [5, 7]]),
...     "b": torch.tensor([4, 3, 2, 1, 0]),
...     "c": torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]),
... }
>>> slice_along_batch(batch, stop=2)
{'a': tensor([[2, 6], [0, 3]]), 'b': tensor([4, 3]), 'c': tensor([1., 2.])}

```

Similarly, it is possible to split a batch in multiple batches by using the
function `split_along_batch`:

```pycon
>>> import torch
>>> from batchtensor.nested import split_along_batch
>>> batch = {
...     "a": torch.tensor([[2, 6], [0, 3], [4, 9], [8, 1], [5, 7]]),
...     "b": torch.tensor([4, 3, 2, 1, 0]),
...     "c": torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]),
... }
>>> split_along_batch(batch, split_size_or_sections=2)
({'a': tensor([[2, 6], [0, 3]]), 'b': tensor([4, 3]), 'c': tensor([1., 2.])},
 {'a': tensor([[4, 9], [8, 1]]), 'b': tensor([2, 1]), 'c': tensor([3., 4.])},
 {'a': tensor([[5, 7]]), 'b': tensor([0]), 'c': tensor([5.])})

```

Please check the documentation to see all the implemented functions.

## Documentation

- [latest (stable)](https://durandtibo.github.io/batchtensor/): documentation from the latest stable
  release.
- [main (unstable)](https://durandtibo.github.io/batchtensor/main/): documentation associated to the
  main branch of the repo. This documentation may contain a lot of work-in-progress/outdated/missing
  parts.

## Installation

We highly recommend installing
a [virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
`batchtensor` can be installed from pip using the following command:

```shell
pip install batchtensor
```

To make the package as slim as possible, only the minimal packages required to use `batchtensor` are
installed.
To include all the dependencies, you can use the following command:

```shell
pip install batchtensor[all]
```

Please check the [get started page](https://durandtibo.github.io/batchtensor/get_started) to see how
to install only some specific dependencies or other alternatives to install the library.
The following is the corresponding `batchtensor` versions and tested dependencies.

| `batchtensor` | `coola`        | `numpy`<sup>*</sup> | `torch`       | `python`       |
|---------------|----------------|---------------------|---------------|----------------|
| `main`        | `>=0.9.1,<1.0` | `>=1.24,<2.0`       | `>=2.4,<3.0`  | `>=3.10,<3.15` |
| `0.1.1`       | `>=0.9.1,<1.0` | `>=1.24,<2.0`       | `>=2.4,<3.0`  | `>=3.10,<3.15` |
| `0.1.0`       | `>=0.8.6,<1.0` | `>=1.21,<2.0`       | `>=2.4,<3.0`  | `>=3.9,<3.14`  |
| `0.0.5`       | `>=0.8.6,<1.0` | `>=1.21,<2.0`       | `>=1.11,<3.0` | `>=3.9,<3.14`  |
| `0.0.4`       | `>=0.1,<1.0`   | `>=1.21,<2.0`       | `>=1.11,<3.0` | `>=3.9,<3.13`  |
| `0.0.3`       | `>=0.1,<1.0`   | `>=1.21,<2.0`       | `>=1.11,<3.0` | `>=3.9,<3.13`  |
| `0.0.2`       | `>=0.1,<1.0`   | `>=1.21,<2.0`       | `>=1.11,<3.0` | `>=3.9,<3.13`  |
| `0.0.1`       | `>=0.1,<0.4`   | `>=1.21,<2.0`       | `>=1.11,<3.0` | `>=3.9,<3.13`  |

<sup>*</sup> indicates an optional dependency

## Contributing

Please check the instructions in [CONTRIBUTING.md](.github/CONTRIBUTING.md).

## Suggestions and Communication

Everyone is welcome to contribute to the community.
If you have any questions or suggestions, you can
submit [Github Issues](https://github.com/durandtibo/batchtensor/issues).
We will reply to you as soon as possible. Thank you very much.

## API stability

:warning: While `batchtensor` is in development stage, no API is guaranteed to be stable from one
release to the next.
In fact, it is very likely that the API will change multiple times before a stable 1.0.0 release.
In practice, this means that upgrading `batchtensor` to a new version will possibly break any code
that was using the old version of `batchtensor`.

## License

`batchtensor` is licensed under BSD 3-Clause "New" or "Revised" license available
in [LICENSE](LICENSE) file.

