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TreeValue is a generalized tree-based data 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.


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

pip install treevalue

For more information about installation, you can refer to the installation guide.


The detailed documentation are hosted on https://opendilab.github.io/treevalue.

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.

from treevalue import FastTreeValue

if __name__ == '__main__':
    t = FastTreeValue({
        'a': 1,
        'b': 2.3,
        'x': {
            'c': 'str',
            'd': [1, 2, None],
            'e': b'bytes',

The result should be

<FastTreeValue 0x7f6c7df00160 keys: ['a', 'b', 'x']>
├── 'a' --> 1
├── 'b' --> 2.3
└── 'x' --> <FastTreeValue 0x7f6c81150860 keys: ['c', 'd', 'e']>
    ├── 'c' --> 'str'
    ├── 'd' --> [1, 2, None]
    └── 'e' --> b'bytes'

And t is structure should be like this

Not only a visible tree structure, but abundant operation supports is provided. You can just put objects (such as torch.Tensor, or any other types) here and just call their methods, like this

import torch

from treevalue import FastTreeValue

t = FastTreeValue({
    'a': torch.rand(2, 5),
    'x': {
        'c': torch.rand(3, 4),

# <FastTreeValue 0x7f8c069346a0>
# ├── a --> tensor([[0.3606, 0.2583, 0.3843, 0.8611, 0.5130],
# │                 [0.0717, 0.1370, 0.1724, 0.7627, 0.7871]])
# └── x --> <FastTreeValue 0x7f8ba6130f40>
#     └── c --> tensor([[0.2320, 0.6050, 0.6844, 0.3609],
#                       [0.0084, 0.0816, 0.8740, 0.3773],
#                       [0.6523, 0.4417, 0.6413, 0.8965]])

print(t.shape)  # property access
# <FastTreeValue 0x7f8c06934ac0>
# ├── a --> torch.Size([2, 5])
# └── x --> <FastTreeValue 0x7f8c069346d0>
#     └── c --> torch.Size([3, 4])
print(t.sin())  # method call
# <FastTreeValue 0x7f8c06934b80>
# ├── a --> tensor([[0.3528, 0.2555, 0.3749, 0.7586, 0.4908],
# │                 [0.0716, 0.1365, 0.1715, 0.6909, 0.7083]])
# └── x --> <FastTreeValue 0x7f8c06934b20>
#     └── c --> tensor([[0.2300, 0.5688, 0.6322, 0.3531],
#                       [0.0084, 0.0816, 0.7669, 0.3684],
#                       [0.6070, 0.4275, 0.5982, 0.7812]])
print(t.reshape((2, -1)))  # method with arguments
# <FastTreeValue 0x7f8c06934b80>
# ├── a --> tensor([[0.3606, 0.2583, 0.3843, 0.8611, 0.5130],
# │                 [0.0717, 0.1370, 0.1724, 0.7627, 0.7871]])
# └── x --> <FastTreeValue 0x7f8c06934b20>
#     └── c --> tensor([[0.2320, 0.6050, 0.6844, 0.3609, 0.0084, 0.0816],
#                       [0.8740, 0.3773, 0.6523, 0.4417, 0.6413, 0.8965]])
print(t[:, 1:-1])  # index operator
# <FastTreeValue 0x7f8ba5c8eca0>
# ├── a --> tensor([[0.2583, 0.3843, 0.8611],
# │                 [0.1370, 0.1724, 0.7627]])
# └── x --> <FastTreeValue 0x7f8ba5c8ebe0>
#     └── c --> tensor([[0.6050, 0.6844],
#                       [0.0816, 0.8740],
#                       [0.4417, 0.6413]])
print(1 + (t - 0.8) ** 2 * 1.5)  # math operators
# <FastTreeValue 0x7fdfa5836b80>
# ├── a --> tensor([[1.6076, 1.0048, 1.0541, 1.3524, 1.0015],
# │                 [1.0413, 1.8352, 1.2328, 1.7904, 1.0088]])
# └── x --> <FastTreeValue 0x7fdfa5836880>
#     └── c --> tensor([[1.1550, 1.0963, 1.3555, 1.2030],
#                       [1.0575, 1.4045, 1.0041, 1.0638],
#                       [1.0782, 1.0037, 1.5075, 1.0658]])

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

Speed Performance

Here is the speed performance of all the operations in FastTreeValue, the following table is the performance comparison result with dm-tree.

flatten flatten(with path) mapping mapping(with path)
treevalue --- 511 ns ± 6.92 ns 3.16 µs ± 42.8 ns 1.58 µs ± 30 ns
flatten flatten_with_path map_structure map_structure_with_path
dm-tree 830 ns ± 8.53 ns 11.9 µs ± 358 ns 13.3 µs ± 87.2 ns 62.9 µs ± 2.26 µs

The following 2 tables are the performance comparison result with jax pytree.

mapping mapping(with path) flatten unflatten flatten_values flatten_keys
treevalue 2.21 µs ± 32.2 ns 2.16 µs ± 123 ns 515 ns ± 7.53 ns 601 ns ± 5.99 ns 301 ns ± 12.9 ns 451 ns ± 17.3 ns
tree_map (Not Implemented) tree_flatten tree_unflatten tree_leaves tree_structure
jax pytree 4.67 µs ± 184 ns --- 1.29 µs ± 27.2 ns 742 ns ± 5.82 ns 1.29 µs ± 22 ns 1.27 µs ± 16.5 ns
flatten + all flatten + reduce flatten + reduce(with init) rise(given structure) rise(automatic structure)
treevalue 425 ns ± 9.33 ns 702 ns ± 5.93 ns 793 ns ± 13.4 ns 9.14 µs ± 129 ns 11.5 µs ± 182 ns
tree_all tree_reduce tree_reduce(with init) tree_transpose (Not Implemented)
jax pytree 1.47 µs ± 37 ns 1.88 µs ± 27.2 ns 1.91 µs ± 47.4 ns 10 µs ± 117 ns ---

This is the comparison between dm-tree, jax-libtree and us, with flatten and mapping operations (lower value means less time cost and runs faster)

Time cost of flatten operation

Time cost of mapping operation

The following table is the performance comparison result with tianshou Batch.

get set init deepcopy stack cat split
treevalue 51.6 ns ± 0.609 ns 64.4 ns ± 0.564 ns 750 ns ± 14.2 ns 88.9 µs ± 887 ns 50.2 µs ± 771 ns 40.3 µs ± 1.08 µs 62 µs ± 1.2 µs
tianshou Batch 43.2 ns ± 0.698 ns 396 ns ± 8.99 ns 11.1 µs ± 277 ns 89 µs ± 1.42 µs 119 µs ± 1.1 µs 194 µs ± 1.81 µs 653 µs ± 17.8 µs

And this is the comparison between tianshou Batch and us, with cat , stack and split operations (lower value means less time cost and runs faster)

Time cost of cat operation

Time cost of stack operation

Time cost of split operation

Test benchmark code can be found here:


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 potc-treevalue

Or just install it with treevalue itself

pip install treevalue[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

For more information, you can refer to


We appreciate all contributions to improve treevalue, 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.


treevalue released under the Apache 2.0 license.

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