pytreeclass

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🛠️ Installation

pip install pytreeclass

Install development version

pip install git+https://github.com/ASEM000/PyTreeClass

📖 Description

PyTreeClass is a JAX-compatible dataclass-like decorator to create and operate on stateful JAX PyTrees.

The package aims to achieve two goals:

🔒 To maintain safe and correct behaviour by using immutable modules with functional API.
To achieve the most intuitive user experience in the JAX ecosystem by :
- 🏗️ Defining layers similar to PyTorch or TensorFlow sublcassing style.
- ☝️ Filtering\Indexing layer values by using boolean masking similar to jax.numpy.at[].{get,set,apply,...}
- 🎨 Visualize defined layers in plethora of ways for better debugging and sharing of information

⏩ Quick Example

🏗️ Create simple MLP

import jax
from jax import numpy as jnp
import pytreeclass as pytc
import matplotlib.pyplot as plt

@pytc.treeclass
class Linear :
   # Any variable not wrapped with @pytc.treeclass
   # should be declared as a dataclass field here
   weight : jnp.ndarray
   bias   : jnp.ndarray

   def __init__(self,key,in_dim,out_dim):
       self.weight = jax.random.normal(key,shape=(in_dim, out_dim)) * jnp.sqrt(2/in_dim)
       self.bias = jnp.ones((1,out_dim))

   def __call__(self,x):
       return x @ self.weight + self.bias

@pytc.treeclass
class StackedLinear:

    def __init__(self,key,in_dim,out_dim,hidden_dim):
        keys= jax.random.split(key,3)

        # Declaring l1,l2,l3 as dataclass_fields is optional
        # as l1,l2,l3 are Linear class that is wrapped with @pytc.treeclass
        # To strictly include nodes defined in dataclass fields 
        # use `@pytc.treeclass(field_only=True)`
        self.l1 = Linear(key=keys[0],in_dim=in_dim,out_dim=hidden_dim)
        self.l2 = Linear(key=keys[1],in_dim=hidden_dim,out_dim=hidden_dim)
        self.l3 = Linear(key=keys[2],in_dim=hidden_dim,out_dim=out_dim)

    def __call__(self,x):
        x = self.l1(x)
        x = jax.nn.tanh(x)
        x = self.l2(x)
        x = jax.nn.tanh(x)
        x = self.l3(x)

        return x
        
model = StackedLinear(in_dim=1,out_dim=1,hidden_dim=10,key=jax.random.PRNGKey(0))

x = jnp.linspace(0,1,100)[:,None]
y = x**3 + jax.random.uniform(jax.random.PRNGKey(0),(100,1))*0.01

🎨 Visualize

summary tree_box tree_diagram

print(model.summary())
┌────┬──────┬───────┬───────┬─────────────────┐
│Name│Type  │Param #│Size   │Config           │
├────┼──────┼───────┼───────┼─────────────────┤
│l1  │Linear│20(0)  │80.00B │weight=f32[1,10] │
│    │      │       │(0.00B)│bias=f32[1,10]   │
├────┼──────┼───────┼───────┼─────────────────┤
│l2  │Linear│110(0) │440.00B│weight=f32[10,10]│
│    │      │       │(0.00B)│bias=f32[1,10]   │
├────┼──────┼───────┼───────┼─────────────────┤
│l3  │Linear│11(0)  │44.00B │weight=f32[10,1] │
│    │      │       │(0.00B)│bias=f32[1,1]    │
└────┴──────┴───────┴───────┴─────────────────┘
Total count :	141(0)
Dynamic count :	141(0)
Frozen count :	0(0)
-----------------------------------------------
Total size :	564.00B(0.00B)
Dynamic size :	564.00B(0.00B)
Frozen size :	0.00B(0.00B)
===============================================

using jax.eval_shape (no-flops operation)

note : the created modules in __init__ should be in the same order where they are called in __call__

print(model.tree_box(array=x))
┌──────────────────────────────────────┐
│StackedLinear[Parent]                 │
├──────────────────────────────────────┤
│┌────────────┬────────┬──────────────┐│
││            │ Input  │ f32[100,1]   ││
││ Linear[l1] │────────┼──────────────┤│
││            │ Output │ f32[100,128] ││
│└────────────┴────────┴──────────────┘│
│┌────────────┬────────┬──────────────┐│
││            │ Input  │ f32[100,128] ││
││ Linear[l2] │────────┼──────────────┤│
││            │ Output │ f32[100,128] ││
│└────────────┴────────┴──────────────┘│
│┌────────────┬────────┬──────────────┐│
││            │ Input  │ f32[100,128] ││
││ Linear[l3] │────────┼──────────────┤│
││            │ Output │ f32[100,1]   ││
│└────────────┴────────┴──────────────┘│
└──────────────────────────────────────┘

print(model.tree_diagram())
StackedLinear
    ├── l1=Linear
    │   ├── weight=f32[1,10]
    │   └── bias=f32[1,10]
    ├── l2=Linear
    │   ├── weight=f32[10,10]
    │   └── bias=f32[1,10]
    └──l3=Linear
        ├── weight=f32[10,1]
        └── bias=f32[1,1]

mermaid.io (Native support in Github/Notion)

# generate mermaid diagrams
# print(pytc.tree_viz.tree_mermaid(model)) # generate core syntax
pytc.tree_viz.save_viz(model,filename="test_mermaid",method="tree_mermaid_md")
# use `method="tree_mermaid_html"` to save as html

flowchart LR
    id15696277213149321320[StackedLinear]
    id15696277213149321320 --> id159132120600507116(l1\nLinear)
    id159132120600507116 --- id7500441386962467209["weight\nf32[1,10]"]
    id159132120600507116 --- id10793958738030044218["bias\nf32[1,10]"]
    id15696277213149321320 --> id10009280772564895168(l2\nLinear)
    id10009280772564895168 --- id11951215191344350637["weight\nf32[10,10]"]
    id10009280772564895168 --- id1196345851686744158["bias\nf32[1,10]"]
    id15696277213149321320 --> id7572222925824649475(l3\nLinear)
    id7572222925824649475 --- id4749243995442935477["weight\nf32[10,1]"]
    id7572222925824649475 --- id8042761346510512486["bias\nf32[1,1]"]

✨ Generate shareable vizualization links ✨

>>> pytc.tree_viz.tree_mermaid(model,link=True)
'Open URL in browser: https://pytreeclass.herokuapp.com/temp/?id=*********'

✂️ Model surgery

# freeze l1
model = model.at["l1"].freeze()

# Set negative_values in l2 to 0
filtered_l2 =  model.l2.at[model.l2<0].set(0) 
model = model.at["l2"].set( filtered_l2 )

# apply sin(x) to all values in l3
filtered_l3 = model.l3.at[...].apply(jnp.sin)
model  = model.at["l3"].set(filtered_l3)

# frozen nodes are marked with #
print(model.tree_diagram())
StackedLinear
    ├#─ l1=Linear
    │   ├#─ weight=f32[1,10]
    │   └#─ bias=f32[1,10]  
    ├── l2=Linear
    │   ├── weight=f32[10,10]
    │   └── bias=f32[1,10]  
    └── l3=Linear
        ├── weight=f32[10,1]
        └── bias=f32[1,1]

☝️ Filtering with `.at[]`

PyTreeClass offers four means of filtering:

Filter by value
Filter by field name
Filter by field type
Filter by field metadata.

The following example demonstrates the usage the filtering. Suppose you have the following (Multilayer perceptron) MLP class

Note in StackedLinear l1 and l2 has a description in field metadata.

Model definition

import jax
from jax import numpy as jnp
import pytreeclass as pytc
import matplotlib.pyplot as plt
from dataclasses import  field 

@pytc.treeclass
class Linear :
   weight : jnp.ndarray
   bias   : jnp.ndarray

   def __init__(self,key,in_dim,out_dim):
       self.weight = jax.random.normal(key,shape=(in_dim, out_dim)) * jnp.sqrt(2/in_dim)
       self.bias = jnp.ones((1,out_dim))

   def __call__(self,x):
       return x @ self.weight + self.bias

@pytc.treeclass
class StackedLinear:
    l1 : Linear = field(metadata={"description": "First layer"})
    l2 : Linear = field(metadata={"description": "Second layer"})

    def __init__(self,key,in_dim,out_dim,hidden_dim):
        keys= jax.random.split(key,3)

        self.l1 = Linear(key=keys[0],in_dim=in_dim,out_dim=hidden_dim)
        self.l2 = Linear(key=keys[2],in_dim=hidden_dim,out_dim=out_dim)

    def __call__(self,x):
        x = self.l1(x)
        x = jax.nn.tanh(x)
        x = self.l2(x)

        return x
        
model = StackedLinear(in_dim=1,out_dim=1,hidden_dim=5,key=jax.random.PRNGKey(0))

Raw model values before any filtering.

print(model)
StackedLinear(
  l1=Linear(
    weight=[[-1.6248673  -2.8383057   1.3969219   1.3169124  -0.40784812]],
    bias=[[1. 1. 1. 1. 1.]]
  ),
  l2=Linear(
    weight=
      [[ 0.98507565]
       [ 0.99815285]
       [-1.0687716 ]
       [-0.19255024]
       [-1.2108876 ]],
    bias=[[1.]]
  )
)

Filter by value

Get all negative values

print(model.at[model<0].get())

StackedLinear(
  l1=Linear(
    weight=[-1.6248673  -2.8383057  -0.40784812],
    bias=[]
  ),
  l2=Linear(
    weight=[-1.0687716  -0.19255024 -1.2108876 ],
    bias=[]
  )
)

Set negative values to 0

print(model.at[model<0].set(0))

StackedLinear(
  l1=Linear(
    weight=[[0.        0.        1.3969219 1.3169124 0.       ]],
    bias=[[1. 1. 1. 1. 1.]]
  ),
  l2=Linear(
    weight=
      [[0.98507565]
       [0.99815285]
       [0.        ]
       [0.        ]
       [0.        ]],
    bias=[[1.]]
  )
)

Apply f(x)=x^2 to negative values

print(model.at[model<0].apply(lambda x:x**2))

StackedLinear(
  l1=Linear(
    weight=[[2.6401937  8.05598    1.3969219  1.3169124  0.16634008]],
    bias=[[1. 1. 1. 1. 1.]]
  ),
  l2=Linear(
    weight=
      [[0.98507565]
       [0.99815285]
       [1.1422727 ]
       [0.03707559]
       [1.4662486 ]],
    bias=[[1.]]
  )
)

Sum all negative values

print(model.at[model<0].reduce(lambda acc,cur: acc+jnp.sum(cur)))
-7.3432307

Filter by field name

Get all fields named l1

print(model.at[model == "l1"].get())

StackedLinear(
  l1=Linear(
    weight=[-1.6248673  -2.8383057   1.3969219   1.3169124  -0.40784812],
    bias=[1. 1. 1. 1. 1.]
  ),
  l2=Linear(weight=[],bias=[])
)

Filter by field type

Get all fields of Linear type

print(model.at[model == Linear].get())

StackedLinear(
  l1=Linear(
    weight=[-1.6248673  -2.8383057   1.3969219   1.3169124  -0.40784812],
    bias=[1. 1. 1. 1. 1.]
  ),
  l2=Linear(
    weight=[ 0.98507565  0.99815285 -1.0687716  -0.19255024 -1.2108876 ],
    bias=[1.]
  )
)

Filter by field metadata

Get all fields of with {"description": "First layer"} in their metadata

print(model.at[model == {"description": "First layer"}].get())

StackedLinear(
  l1=Linear(
    weight=[-1.6248673  -2.8383057   1.3969219   1.3169124  -0.40784812],
    bias=[1. 1. 1. 1. 1.]
  ),
  l2=Linear(weight=[],bias=[])
)

Mix and match different filtering methods.

Get only fields named weight positive values.

mask = (model == "weight") & (model>0)
print(model.at[mask].get())

StackedLinear(
  l1=Linear(weight=[1.3969219 1.3169124],bias=[]),
  l2=Linear(weight=[0.98507565 0.99815285],bias=[])
)

📝 Applications

Physics informed neural network (PINN)

🔢 More

More compact boilerplate

Using param:

More compact definition can be done with node defined at runtime call.
The Linear layers are defined on the first call and retrieved on the subsequent calls
This pattern is useful if the module definition depends on runtime data.

@pytc.treeclass
class StackedLinear:
    keys: Any

    def __init__(self,key):
        self.keys = jax.random.split(key,3)

    def __call__(self,x):
        x = self.param(Linear(self.keys[0],x.shape[-1],10),name="l1")(x)
        x = jax.nn.tanh(x)
        x = self.param(Linear(self.keys[1],10,10),name="l2")(x)
        x = jax.nn.tanh(x)
        x = self.param(Linear(self.keys[2],10,x.shape[-1]),name="l3")(x)
        return x

# Upon defining the layer the modules are not instantiated
# However, after first call , the nodes are defined.
model = StackedLinear(jax.random.PRNGKey(0))
print(model)

StackedLinear(keys=ui32[3,2])

model((jnp.ones((10,10)))) # first call
print(f"{model!r}")

StackedLinear(
  keys=ui32[3,2],
  l1=Linear(weight=f32[10,10],bias=f32[1,10]),
  l2=Linear(weight=f32[10,10],bias=f32[1,10]),
  l3=Linear(weight=f32[10,10],bias=f32[1,10])
)

📙 Acknowledgements

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