https://github.com/opendilab/di-treetensor
Let DI-treetensor help you simplify the structure processing!（树形运算一不小心就逻辑混乱？DI-treetensor快速帮你搞定）
https://github.com/opendilab/di-treetensor
deep-learning numpy python pytorch reinforcement-learning tensor tree-structure
Last synced: about 2 months ago
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Let DI-treetensor help you simplify the structure processing!（树形运算一不小心就逻辑混乱？DI-treetensor快速帮你搞定）
- Host: GitHub
- URL: https://github.com/opendilab/di-treetensor
- Owner: opendilab
- License: apache-2.0
- Created: 2021-09-04T15:56:36.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-21T09:05:42.000Z (9 months ago)
- Last Synced: 2025-04-01T11:04:37.551Z (3 months ago)
- Topics: deep-learning, numpy, python, pytorch, reinforcement-learning, tensor, tree-structure
- Language: Python
- Homepage: https://opendilab.github.io/DI-treetensor/
- Size: 74.5 MB
- Stars: 206
- Watchers: 3
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
---
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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
├── a --> tensor([[-1.2672, -1.5817, -0.3141],
│ [ 1.8107, -0.1023, 0.0940]])
└── 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]])
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:
└── 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:
├── a --> tensor([[1.6057, 2.5018, 0.0986],
│ [3.2786, 0.0105, 0.0088]])
└── b -->
└── 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()
├── a --> tensor([[0.5782, 0.5404, 0.9527],
│ [0.5642, 0.9948, 0.9956]])
└── b -->
└── 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:
├── a --> tensor([[-0.0435, -0.0535, -0.0131],
│ [ 0.0545, -0.0064, -0.0002]])
└── b -->
└── 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=)
```
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
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.