DI-treetensor 0.5.0
A flexible, generalized tree-based tensor structure.
License: Apache License, Version 2.0
Requires Python: >=3.8
Keywords: Tree-structured Value Management

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---

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`treetensor` is a generalized tree-based tensor structure mainly developed by [OpenDILab Contributors](https://github.com/opendilab).

Almost all the operation can be supported in form of trees in a convenient way to simplify the structure processing when the calculation is tree-based.

## Installation

You can simply install it with `pip` command line from the official PyPI site.

```shell
pip install di-treetensor
```

For more information about installation, you can refer to [Installation](https://opendilab.github.io/DI-treetensor/main/tutorials/installation/index.html#).

## Documentation

The detailed documentation are hosted on [https://opendilab.github.io/DI-treetensor](https://opendilab.github.io/DI-treetensor/).

Only english version is provided now, the chinese documentation is still under development.

## Quick Start

You can easily create a tree value object based on `FastTreeValue`.

```python
import builtins
import os
from functools import partial

import treetensor.torch as torch

print = partial(builtins.print, sep=os.linesep)

if __name__ == '__main__':
    # create a tree tensor
    t = torch.randn({'a': (2, 3), 'b': {'x': (3, 4)}})
    print(t)
    print(torch.randn(4, 5))  # create a normal tensor
    print()

    # structure of tree
    print('Structure of tree')
    print('t.a:', t.a)  # t.a is a native tensor
    print('t.b:', t.b)  # t.b is a tree tensor
    print('t.b.x', t.b.x)  # t.b.x is a native tensor
    print()

    # math calculations
    print('Math calculation')
    print('t ** 2:', t ** 2)
    print('torch.sin(t).cos()', torch.sin(t).cos())
    print()

    # backward calculation
    print('Backward calculation')
    t.requires_grad_(True)
    t.std().arctan().backward()
    print('grad of t:', t.grad)
    print()

    # native operation
    # all the ops can be used as the original usage of `torch`
    print('Native operation')
    print('torch.sin(t.a)', torch.sin(t.a))  # sin of native tensor

```

The result should be

```text
<Tensor 0x7f0dae602760>
├── a --> tensor([[-1.2672, -1.5817, -0.3141],
│                 [ 1.8107, -0.1023,  0.0940]])
└── b --> <Tensor 0x7f0dae602820>
    └── x --> tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
                      [ 1.5956,  0.8825, -0.5702, -0.2247],
                      [ 0.9235,  0.4538,  0.8775, -0.2642]])

tensor([[-0.9559,  0.7684,  0.2682, -0.6419,  0.8637],
        [ 0.9526,  0.2927, -0.0591,  1.2804, -0.2455],
        [ 0.4699, -0.9998,  0.6324, -0.6885,  1.1488],
        [ 0.8920,  0.4401, -0.7785,  0.5931,  0.0435]])

Structure of tree
t.a:
tensor([[-1.2672, -1.5817, -0.3141],
        [ 1.8107, -0.1023,  0.0940]])
t.b:
<Tensor 0x7f0dae602820>
└── x --> tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
                  [ 1.5956,  0.8825, -0.5702, -0.2247],
                  [ 0.9235,  0.4538,  0.8775, -0.2642]])

t.b.x
tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
        [ 1.5956,  0.8825, -0.5702, -0.2247],
        [ 0.9235,  0.4538,  0.8775, -0.2642]])

Math calculation
t ** 2:
<Tensor 0x7f0dae602eb0>
├── a --> tensor([[1.6057, 2.5018, 0.0986],
│                 [3.2786, 0.0105, 0.0088]])
└── b --> <Tensor 0x7f0dae60c040>
    └── x --> tensor([[1.4943, 0.1187, 0.9960, 0.1669],
                      [2.5458, 0.7789, 0.3252, 0.0505],
                      [0.8528, 0.2059, 0.7699, 0.0698]])

torch.sin(t).cos()
<Tensor 0x7f0dae621910>
├── a --> tensor([[0.5782, 0.5404, 0.9527],
│                 [0.5642, 0.9948, 0.9956]])
└── b --> <Tensor 0x7f0dae6216a0>
    └── x --> tensor([[0.5898, 0.9435, 0.6672, 0.9221],
                      [0.5406, 0.7163, 0.8578, 0.9753],
                      [0.6983, 0.9054, 0.7185, 0.9661]])


Backward calculation
grad of t:
<Tensor 0x7f0dae60c400>
├── a --> tensor([[-0.0435, -0.0535, -0.0131],
│                 [ 0.0545, -0.0064, -0.0002]])
└── b --> <Tensor 0x7f0dae60cbe0>
    └── x --> tensor([[ 0.0357, -0.0141, -0.0349, -0.0162],
                      [ 0.0476,  0.0249, -0.0213, -0.0103],
                      [ 0.0262,  0.0113,  0.0248, -0.0116]])


Native operation
torch.sin(t.a)
tensor([[-0.9543, -0.9999, -0.3089],
        [ 0.9714, -0.1021,  0.0939]], grad_fn=<SinBackward>)

```

For more quick start explanation and further usage, take a look at:

* [Quick Start](https://opendilab.github.io/DI-treetensor/main/tutorials/quick_start/index.html)

## Extension

If you need to translate `treevalue` object to runnable source code, you may use the [potc-treevalue](https://github.com/potc-dev/potc-treevalue) plugin with the installation command below

```
pip install DI-treetensor[potc]
```

In potc, you can translate the objects to runnable python source code, which can be loaded to objects afterwards by the python interpreter, like the following graph

![potc_system](https://github.com/opendilab/DI-treetensor/blob/main/docs/source/_static/potc-doing.svg)

For more information, you can refer to

- [potc-dev/potc](https://github.com/potc-dev/potc)
- [potc-dev/potc-treevalue](https://github.com/potc-dev/potc-treevalue)
- [potc-dev/potc-torch](https://github.com/potc-dev/potc-torch)
- [Potc Plugin Installation](https://opendilab.github.io/DI-treetensor/main/tutorials/plugins/index.html#potc-support)

## Contribution

We appreciate all contributions to improve DI-treetensor, both logic and system designs. Please refer to CONTRIBUTING.md for more guides.

And users can join our [slack communication channel](https://join.slack.com/t/opendilab/shared_invite/zt-v9tmv4fp-nUBAQEH1_Kuyu_q4plBssQ), or contact the core developer [HansBug](https://github.com/HansBug) for more detailed discussion.

## License

`DI-treetensor` released under the Apache 2.0 license.