# 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 This documentation may contain a lot of work-in-progress/outdated/missing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` | `torch` | `python` |
|---------------|--------------|---------------|---------------|
| `main` | `>=0.1,<1.0` | `>=1.11,<3.0` | `>=3.9,<3.13` |
| `0.0.3` | `>=0.1,<1.0` | `>=1.11,<3.0` | `>=3.9,<3.13` |
| `0.0.2` | `>=0.1,<1.0` | `>=1.11,<3.0` | `>=3.9,<3.13` |
| `0.0.1` | `>=0.1,<0.4` | `>=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.
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href=\"https://pepy.tech/project/batchtensor\">\n <img alt=\"Monthly downloads\" src=\"https://static.pepy.tech/badge/batchtensor/month\">\n </a>\n <br/>\n</p>\n\n## Overview\n\n`batchtensor` is lightweight library built on top of [PyTorch](https://pytorch.org/) to manipulate\nnested data structure with PyTorch tensors.\nThis library provides functions for tensors where the first dimension is the batch dimension.\nIt also provides functions for tensors representing a batch of sequences where the first dimension\nis the batch dimension and the second dimension is the sequence dimension.\n\n- [Motivation](#motivation)\n- [Documentation](https://durandtibo.github.io/batchtensor/)\n- [Installation](#installation)\n- [Contributing](#contributing)\n- [API stability](#api-stability)\n- [License](#license)\n\n## Motivation\n\nLet's imagine you have a batch which is represented by a dictionary with three tensors, and you want\nto take the first 2 items.\n`batchtensor` provides the function `slice_along_batch` that allows to slide all the tensors:\n\n```pycon\n>>> import torch\n>>> from batchtensor.nested import slice_along_batch\n>>> batch = {\n... \"a\": torch.tensor([[2, 6], [0, 3], [4, 9], [8, 1], [5, 7]]),\n... \"b\": torch.tensor([4, 3, 2, 1, 0]),\n... \"c\": torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]),\n... }\n>>> slice_along_batch(batch, stop=2)\n{'a': tensor([[2, 6], [0, 3]]), 'b': tensor([4, 3]), 'c': tensor([1., 2.])}\n\n```\n\nSimilarly, it is possible to split a batch in multiple batches by using the\nfunction `split_along_batch`:\n\n```pycon\n>>> import torch\n>>> from batchtensor.nested import split_along_batch\n>>> batch = {\n... \"a\": torch.tensor([[2, 6], [0, 3], [4, 9], [8, 1], [5, 7]]),\n... \"b\": torch.tensor([4, 3, 2, 1, 0]),\n... \"c\": torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]),\n... }\n>>> split_along_batch(batch, split_size_or_sections=2)\n({'a': tensor([[2, 6], [0, 3]]), 'b': tensor([4, 3]), 'c': tensor([1., 2.])},\n {'a': tensor([[4, 9], [8, 1]]), 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This documentation may contain a lot of work-in-progress/outdated/missing\n parts.\n\n## Installation\n\nWe highly recommend installing\na [virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).\n`batchtensor` can be installed from pip using the following command:\n\n```shell\npip install batchtensor\n```\n\nTo make the package as slim as possible, only the minimal packages required to use `batchtensor` are\ninstalled.\nTo include all the dependencies, you can use the following command:\n\n```shell\npip install batchtensor[all]\n```\n\nPlease check the [get started page](https://durandtibo.github.io/batchtensor/get_started) to see how\nto install only some specific dependencies or other alternatives to install the library.\nThe following is the corresponding `batchtensor` versions and tested dependencies.\n\n| `batchtensor` | `coola` | `torch` | `python` |\n|---------------|--------------|---------------|---------------|\n| `main` | `>=0.1,<1.0` | `>=1.11,<3.0` | `>=3.9,<3.13` |\n| `0.0.3` | `>=0.1,<1.0` | `>=1.11,<3.0` | `>=3.9,<3.13` |\n| `0.0.2` | `>=0.1,<1.0` | `>=1.11,<3.0` | `>=3.9,<3.13` |\n| `0.0.1` | `>=0.1,<0.4` | `>=1.11,<3.0` | `>=3.9,<3.13` |\n\n<sup>*</sup> indicates an optional dependency\n\n## Contributing\n\nPlease check the instructions in [CONTRIBUTING.md](.github/CONTRIBUTING.md).\n\n## Suggestions and Communication\n\nEveryone is welcome to contribute to the community.\nIf you have any questions or suggestions, you can\nsubmit [Github Issues](https://github.com/durandtibo/batchtensor/issues).\nWe will reply to you as soon as possible. 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