| Name | pytreeclass JSON |
| Version |
0.11.0
JSON |
| download |
| home_page | None |
| Summary | Visualize, create, and operate on pytrees in the most intuitive way possible. |
| upload_time | 2025-01-11 19:13:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2022 Mahmoud Asem Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
jax
neural-networks
functional-programming
machine-learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
<!-- <h1 align="center" style="font-family:Monospace" >Pyπ²Class</h1> -->
<h5 align="center">
<img width="250px" src="https://github.com/ASEM000/pytreeclass/assets/48389287/95e879f2-69d9-420b-bb64-012fa0b4eeb8"> <br>
<br>
[**Installation**](#installation)
|[**Description**](#description)
|[**Quick Example**](#quick_example)
|[**StatefulComputation**](#stateful_computation)
|[**Benchamrks**](#more)
|[**Acknowledgements**](#acknowledgements)
[](https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/intro.ipynb)
[](https://pepy.tech/project/pytreeclass)
[](https://codecov.io/gh/ASEM000/pytreeclass)
[](https://pytreeclass.readthedocs.io/en/latest/?badge=latest)
[](https://zenodo.org/badge/latestdoi/512717921)
[](https://www.codefactor.io/repository/github/asem000/pytreeclass)
</h5>
## π οΈ Installation<a id="installation"></a>
```python
pip install pytreeclass
```
**Install development version**
```python
pip install git+https://github.com/ASEM000/pytreeclass
```
## π Description<a id="description"></a>
`pytreeclass` is a JAX-compatible class builder to create and operate on stateful JAX PyTrees in a performant and intuitive way, by building on familiar concepts found in `numpy`, `dataclasses`, and others.
See [documentation](https://pytreeclass.readthedocs.io/en/latest/notebooks/getting_started.html) and [π³ Common recipes](https://pytreeclass.readthedocs.io/en/latest/notebooks/common_recipes.html) to check if this library is a good fit for your work. _If you find the package useful consider giving it a π._
## β© Quick Example <a id="quick_example">
<div align="center">
<table>
<tr><td align="center"></td></tr>
<tr>
<td>
```python
import jax
import jax.numpy as jnp
import pytreeclass as tc
@tc.autoinit
class Tree(tc.TreeClass):
a: float = 1.0
b: tuple[float, float] = (2.0, 3.0)
c: jax.Array = jnp.array([4.0, 5.0, 6.0])
def __call__(self, x):
return self.a + self.b[0] + self.c + x
tree = Tree()
mask = jax.tree_map(lambda x: x > 5, tree)
tree = tree\
.at["a"].set(100.0)\
.at["b"][0].set(10.0)\
.at[mask].set(100.0)
print(tree)
# Tree(a=100.0, b=(10.0, 3.0), c=[ 4. 5. 100.])
print(tc.tree_diagram(tree))
# Tree
# βββ .a=100.0
# βββ .b:tuple
# β βββ [0]=10.0
# β βββ [1]=3.0
# βββ .c=f32[3](ΞΌ=36.33, Ο=45.02, β[4.00,100.00])
print(tc.tree_summary(tree))
# βββββββ¬βββββββ¬ββββββ¬βββββββ
# βName βType βCountβSize β
# βββββββΌβββββββΌββββββΌβββββββ€
# β.a βfloat β1 β β
# βββββββΌβββββββΌββββββΌβββββββ€
# β.b[0]βfloat β1 β β
# βββββββΌβββββββΌββββββΌβββββββ€
# β.b[1]βfloat β1 β β
# βββββββΌβββββββΌββββββΌβββββββ€
# β.c βf32[3]β3 β12.00Bβ
# βββββββΌβββββββΌββββββΌβββββββ€
# βΞ£ βTree β6 β12.00Bβ
# βββββββ΄βββββββ΄ββββββ΄βββββββ
# ** pass it to jax transformations **
# works with jit, grad, vmap, etc.
@jax.jit
@jax.grad
def sum_tree(tree: Tree, x):
return sum(tree(x))
print(sum_tree(tree, 1.0))
# Tree(a=3.0, b=(3.0, 0.0), c=[1. 1. 1.])
```
</td>
</tr>
</table>
</div>
## π Stateful computations<a id="stateful_computation"></a>
[Under jax.jit jax requires states to be explicit](https://jax.readthedocs.io/en/latest/jax-101/07-state.html?highlight=state), this means that for any class instance; variables needs to be separated from the class and be passed explictly. However when using `TreeClass` no need to separate the instance variables ; instead the whole instance is passed as a state.
Using the following pattern,Updating state **functionally** can be achieved under `jax.jit`
<div align="center">
<table>
<tr><td align="center"></td></tr>
<tr>
<td>
```python
import jax
import pytreeclass as tc
class Counter(tc.TreeClass):
def __init__(self, calls: int = 0):
self.calls = calls
def increment(self):
self.calls += 1
counter = Counter() # Counter(calls=0)
```
</td>
</tr>
</table>
</div>
Here, we define the update function. Since the increment method mutate the internal state, thus we need to use the functional approach to update the state by using `.at`. To achieve this we can use `.at[method_name].__call__(*args,**kwargs)`, this functional call will return the value of this call and a _new_ model instance with the update state.
<div align="center">
<table>
<tr><td align="center"></td></tr>
<tr>
<td>
```python
@jax.jit
def update(counter):
value, new_counter = counter.at["increment"]()
return new_counter
for i in range(10):
counter = update(counter)
print(counter.calls) # 10
```
</td>
</tr>
</table>
</div>
</details>
## β Benchmarks<a id="more"></a>
<details>
<summary>Benchmark flatten/unflatten compared to Flax and Equinox </summary>
<a href="https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/benchmark_flatten_unflatten.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<table>
<tr><td align="center">CPU</td><td align="center">GPU</td></tr>
<tr>
<td><img src='assets/benchmark_cpu.png'></td>
</tr>
</table>
</details>
<details>
<summary>Benchmark simple training against `flax` and `equinox` </summary>
Training simple sequential linear benchmark against `flax` and `equinox`
<table>
<tr>
<td align="center">Num of layers</td>
<td align="center">Flax/tc time<br><a href="https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/benchmark_nn_training_flax.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a></td>
<td align="center">Equinox/tc time<br> <a href="https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/benchmark_nn_training_equinox.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a></td>
</tr>
<tr>
<td align="center">10</td>
<td align="center">1.427</td>
<td align="center">6.671</td>
</tr>
<tr>
<td align="center">100</td>
<td align="center">1.1130</td>
<td align="center">2.714</td>
</tr>
</table>
</details>
## π Acknowledgements<a id="acknowledgements"></a>
- [Lenses](https://hackage.haskell.org/package/lens)
- [Treex](https://github.com/cgarciae/treex), [Equinox](https://github.com/patrick-kidger/equinox), [tree-math](https://github.com/google/tree-math), [Flax PyTreeNode](https://github.com/google/flax/commit/291a5f65549cf4522f0de033451cd83c0d0168d9), [TensorFlow](https://www.tensorflow.org), [PyTorch](https://pytorch.org)
- [Lovely JAX](https://github.com/xl0/lovely-jax)
Raw data
{
"_id": null,
"home_page": null,
"name": "pytreeclass",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "jax, neural-networks, functional-programming, machine-learning",
"author": null,
"author_email": "Mahmoud Asem <mahmoudasem00@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/df/9c/01c7be29a30728d890a80ffc0b44b99c465c35d5b6c898db6e116ed0665d/pytreeclass-0.11.0.tar.gz",
"platform": null,
"description": "<!-- <h1 align=\"center\" style=\"font-family:Monospace\" >Py\ud83c\udf32Class</h1> -->\n<h5 align=\"center\">\n<img width=\"250px\" src=\"https://github.com/ASEM000/pytreeclass/assets/48389287/95e879f2-69d9-420b-bb64-012fa0b4eeb8\"> <br>\n\n<br>\n\n[**Installation**](#installation)\n|[**Description**](#description)\n|[**Quick Example**](#quick_example)\n|[**StatefulComputation**](#stateful_computation)\n|[**Benchamrks**](#more)\n|[**Acknowledgements**](#acknowledgements)\n\n\n\n\n\n\n\n[](https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/intro.ipynb)\n[](https://pepy.tech/project/pytreeclass)\n[](https://codecov.io/gh/ASEM000/pytreeclass)\n[](https://pytreeclass.readthedocs.io/en/latest/?badge=latest)\n\n[](https://zenodo.org/badge/latestdoi/512717921)\n\n[](https://www.codefactor.io/repository/github/asem000/pytreeclass)\n\n</h5>\n\n## \ud83d\udee0\ufe0f Installation<a id=\"installation\"></a>\n\n```python\npip install pytreeclass\n```\n\n**Install development version**\n\n```python\npip install git+https://github.com/ASEM000/pytreeclass\n```\n\n## \ud83d\udcd6 Description<a id=\"description\"></a>\n\n`pytreeclass` is a JAX-compatible class builder to create and operate on stateful JAX PyTrees in a performant and intuitive way, by building on familiar concepts found in `numpy`, `dataclasses`, and others.\n\nSee [documentation](https://pytreeclass.readthedocs.io/en/latest/notebooks/getting_started.html) and [\ud83c\udf73 Common recipes](https://pytreeclass.readthedocs.io/en/latest/notebooks/common_recipes.html) to check if this library is a good fit for your work. _If you find the package useful consider giving it a \ud83c\udf1f._\n\n## \u23e9 Quick Example <a id=\"quick_example\">\n\n<div align=\"center\">\n<table>\n<tr><td align=\"center\"></td></tr>\n<tr>\n<td>\n\n```python\nimport jax\nimport jax.numpy as jnp\nimport pytreeclass as tc\n\n@tc.autoinit\nclass Tree(tc.TreeClass):\n a: float = 1.0\n b: tuple[float, float] = (2.0, 3.0)\n c: jax.Array = jnp.array([4.0, 5.0, 6.0])\n\n def __call__(self, x):\n return self.a + self.b[0] + self.c + x\n\n\ntree = Tree()\nmask = jax.tree_map(lambda x: x > 5, tree)\ntree = tree\\\n .at[\"a\"].set(100.0)\\\n .at[\"b\"][0].set(10.0)\\\n .at[mask].set(100.0)\n\nprint(tree)\n# Tree(a=100.0, b=(10.0, 3.0), c=[ 4. 5. 100.])\n\nprint(tc.tree_diagram(tree))\n# Tree\n# \u251c\u2500\u2500 .a=100.0\n# \u251c\u2500\u2500 .b:tuple\n# \u2502 \u251c\u2500\u2500 [0]=10.0\n# \u2502 \u2514\u2500\u2500 [1]=3.0\n# \u2514\u2500\u2500 .c=f32[3](\u03bc=36.33, \u03c3=45.02, \u2208[4.00,100.00])\n\nprint(tc.tree_summary(tree))\n# \u250c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502Name \u2502Type \u2502Count\u2502Size \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502.a \u2502float \u25021 \u2502 \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502.b[0]\u2502float \u25021 \u2502 \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502.b[1]\u2502float \u25021 \u2502 \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502.c \u2502f32[3]\u25023 \u250212.00B\u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502\u03a3 \u2502Tree \u25026 \u250212.00B\u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n# ** pass it to jax transformations **\n# works with jit, grad, vmap, etc.\n\n@jax.jit\n@jax.grad\ndef sum_tree(tree: Tree, x):\n return sum(tree(x))\n\nprint(sum_tree(tree, 1.0))\n# Tree(a=3.0, b=(3.0, 0.0), c=[1. 1. 1.])\n```\n\n</td>\n\n</tr>\n</table>\n</div>\n\n## \ud83d\udcdc Stateful computations<a id=\"stateful_computation\"></a>\n\n[Under jax.jit jax requires states to be explicit](https://jax.readthedocs.io/en/latest/jax-101/07-state.html?highlight=state), this means that for any class instance; variables needs to be separated from the class and be passed explictly. However when using `TreeClass` no need to separate the instance variables ; instead the whole instance is passed as a state.\n\nUsing the following pattern,Updating state **functionally** can be achieved under `jax.jit`\n\n<div align=\"center\">\n<table>\n<tr><td align=\"center\"></td></tr>\n<tr>\n<td>\n\n```python\nimport jax\nimport pytreeclass as tc\n\nclass Counter(tc.TreeClass):\n def __init__(self, calls: int = 0):\n self.calls = calls\n\n def increment(self):\n self.calls += 1\ncounter = Counter() # Counter(calls=0)\n```\n\n</td>\n\n</tr>\n</table>\n</div>\n\nHere, we define the update function. Since the increment method mutate the internal state, thus we need to use the functional approach to update the state by using `.at`. To achieve this we can use `.at[method_name].__call__(*args,**kwargs)`, this functional call will return the value of this call and a _new_ model instance with the update state.\n\n<div align=\"center\">\n<table>\n<tr><td align=\"center\"></td></tr>\n<tr>\n<td>\n\n```python\n@jax.jit\ndef update(counter):\n value, new_counter = counter.at[\"increment\"]()\n return new_counter\n\nfor i in range(10):\n counter = update(counter)\n\nprint(counter.calls) # 10\n```\n\n</td>\n\n</tr>\n</table>\n</div>\n\n</details>\n\n## \u2795 Benchmarks<a id=\"more\"></a>\n\n<details>\n<summary>Benchmark flatten/unflatten compared to Flax and Equinox </summary>\n\n<a href=\"https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/benchmark_flatten_unflatten.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n\n<table>\n\n<tr><td align=\"center\">CPU</td><td align=\"center\">GPU</td></tr>\n\n<tr>\n\n<td><img src='assets/benchmark_cpu.png'></td>\n\n</tr>\n\n</table>\n\n</details>\n\n<details>\n\n<summary>Benchmark simple training against `flax` and `equinox` </summary>\n\nTraining simple sequential linear benchmark against `flax` and `equinox`\n\n<table>\n\n<tr>\n<td align=\"center\">Num of layers</td>\n<td align=\"center\">Flax/tc time<br><a href=\"https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/benchmark_nn_training_flax.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a></td>\n<td align=\"center\">Equinox/tc time<br> <a href=\"https://colab.research.google.com/github/ASEM000/pytreeclass/blob/main/assets/benchmark_nn_training_equinox.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a></td>\n</tr>\n\n<tr>\n<td align=\"center\">10</td>\n<td align=\"center\">1.427</td>\n<td align=\"center\">6.671</td>\n</tr>\n\n<tr>\n<td align=\"center\">100</td>\n<td align=\"center\">1.1130</td>\n<td align=\"center\">2.714</td>\n</tr>\n\n</table>\n\n</details>\n\n## \ud83d\udcd9 Acknowledgements<a id=\"acknowledgements\"></a>\n\n- [Lenses](https://hackage.haskell.org/package/lens)\n- [Treex](https://github.com/cgarciae/treex), [Equinox](https://github.com/patrick-kidger/equinox), [tree-math](https://github.com/google/tree-math), [Flax PyTreeNode](https://github.com/google/flax/commit/291a5f65549cf4522f0de033451cd83c0d0168d9), [TensorFlow](https://www.tensorflow.org), [PyTorch](https://pytorch.org)\n- [Lovely JAX](https://github.com/xl0/lovely-jax)\n",
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