DI-treetensor
treetensor is a generalized tree-based tensor structure mainly developed by OpenDILab Contributors.
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.
pip install di-treetensor
For more information about installation, you can refer to Installation.
Documentation
The detailed documentation are hosted on 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.
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
<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:
Extension
If you need to translate treevalue object to runnable source code, you may use the 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
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, or contact the core developer HansBug for more detailed discussion.
License
DI-treetensor released under the Apache 2.0 license.
1.2k Dec 29, 2022
536 Jan 6, 2023
5 May 23, 2022
127 Jan 5, 2023
4 Jun 17, 2022
7 Apr 23, 2022
205 Dec 29, 2022
48 Dec 26, 2022
1 Feb 15, 2022
5.4k Jan 02, 2023
34 Nov 24, 2022
4 Dec 25, 2022
4 Dec 22, 2022
2.5k Dec 29, 2022
1 Jan 13, 2022
35 Oct 22, 2022
47 Dec 16, 2022
16 Nov 21, 2022
32 Dec 27, 2022
4 Sep 25, 2022
1 Dec 10, 2021
175 Dec 25, 2022
3 Nov 01, 2022
2.3k Jan 09, 2023
7 Aug 12, 2022
1 Jan 25, 2022
4 Nov 14, 2022
3 Dec 23, 2021