pytreeclass 0.0.6.post0

JAX compatible dataclass.

🌲Pytreeclass🌲

Write pytorch-like layers with rich visualizations in JAX.

Installation |Description |Quick Example |StatefulComputation |More |Applications |Acknowledgements

🛠️ Installation

pip install pytreeclass

📖 Description

A JAX compatible dataclass like datastructure with the following functionalities

• Create PyTorch like NN classes
• Provides rich visualizations for pytrees wrapped with @pytc.treeclass.
• Boolean indexing on Pytrees in functional style similar to jax.numpy. e.g. x.at[x<0].set(0)
• Apply math/numpy operations on pytrees

⏩ 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
        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()) ┌──────┬───────┬───────┬─────────────────┐ │Type │Param #│Size │Config │ ├──────┼───────┼───────┼─────────────────┤ │Linear│20 │80.00B │weight=f32[1,10] │ │ │(0) │(0.00B)│bias=f32[1,10] │ ├──────┼───────┼───────┼─────────────────┤ │Linear│110 │440.00B│weight=f32[10,10]│ │ │(0) │(0.00B)│bias=f32[1,10] │ ├──────┼───────┼───────┼─────────────────┤ │Linear│11 │44.00B │weight=f32[10,1] │ │ │(0) │(0.00B)│bias=f32[1,1] │ └──────┴───────┴───────┴─────────────────┘ Total # : 141(0) Dynamic #: 141(0) Static/Frozen #: 0(0) ------------------------------------------ Total size : 564.00B(0.00B) Dynamic size: 564.00B(0.00B) Static/Frozen size: 0.00B(0.00B) ==========================================	>>> print(model.tree_box(array=x)) # 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__ ┌─────────────────────────────────────┐ │StackedLinear(Parent) │ ├─────────────────────────────────────┤ │┌────────────┬────────┬─────────────┐│ ││ │ Input │ f32[100,1] ││ ││ Linear(l1) │────────┼─────────────┤│ ││ │ Output │ f32[100,10] ││ │└────────────┴────────┴─────────────┘│ │┌────────────┬────────┬─────────────┐│ ││ │ Input │ f32[100,10] ││ ││ Linear(l2) │────────┼─────────────┤│ ││ │ Output │ f32[100,10] ││ │└────────────┴────────┴─────────────┘│ │┌────────────┬────────┬─────────────┐│ ││ │ Input │ f32[100,10] ││ ││ 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 TD 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.l1 = model.l1.freeze()

# set negative values in l2 to 0
>>> model.l2 = model.l2.at[model.l2<0].set(0)

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

# 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] 

📜 Stateful computations

JAX reference

Under jax.jit jax requires states to be explicit, this means that for any class instance; variables needs to be separated from the class and be passed explictly. However when using @pytc.treeclass no need to separate the instance variables ; instead the whole instance is passed as a state.

The following code snippets compares between the two concepts by comparing MLP's implementation.

Explicit state

Class instance as state

import jax.numpy as jnp import jax.random as jr from jax.nn.initializers import he_normal from jax.tree_util import tree_map from jax import nn, value_and_grad,jit import pytreeclass as pytc def init_params(layers): keys = jr.split( jr.PRNGKey(0),len(layers)-1 ) params = list() init_func = he_normal() for key,n_in,n_out in zip( keys, layers[:-1], layers[1:] ): W = init_func(key,(n_in,n_out)) B = jr.uniform(key,shape=(n_out,)) params.append({'W':W,'B':B}) return params def fwd(params,x): *hidden,last = params for layer in hidden : x = nn.tanh(x@layer['W']+layer['B']) return x@last['W'] + last['B'] @value_and_grad def loss_func(params,x,y): pred = fwd(params,x) return jnp.mean((pred-y)**2) @jit def update(params,x,y): # gradient w.r.t to params value,grads= loss_func(params,x,y) params = tree_map( lambda x,y : x-1e-3*y, params,grads ) return value,params x = jnp.linspace(0,1,100).reshape(100,1) y = x**2 -1 params = init_params([1] +[5]*4+[1] ) epochs = 10_000 for _ in range(1,epochs+1): value , params = update(params,x,y) # print loss and epoch info if _ %(1_000) ==0: print(f'Epoch={_}\tloss={value:.3e}')

import jax.numpy as jnp import jax.random as jr from jax.nn.initializers import he_normal from jax.tree_util import tree_map from jax import nn, value_and_grad,jit import pytreeclass as pytc @pytc.treeclass class MLP: Layers : list def __init__(self,layers): keys = jr.split( jr.PRNGKey(0),len(layers)-1 ) self.Layers = list() init_func = he_normal() for key,n_in,n_out in zip( keys, layers[:-1], layers[1:] ): W = init_func(key,(n_in,n_out)) B = jr.uniform(key,shape=(n_out,)) self.Layers.append({'W':W,'B':B}) def __call__(self,x): *hidden,last = self.Layers for layer in hidden : x = nn.tanh(x@layer['W']+layer['B']) return x@last['W'] + last['B'] @value_and_grad def loss_func(model,x,y): pred = model(x) return jnp.mean((pred-y)**2) @jit def update(model,x,y): # gradient w.r.t to model value , grads= loss_func(model,x,y) model = tree_map( lambda x,y : x-1e-3*y, model,grads ) return value , model x = jnp.linspace(0,1,100).reshape(100,1) y = x**2 -1 model = MLP([1] +[5]*4+[1] ) epochs = 10_000 for _ in range(1,epochs+1): value , model = update(model,x,y) # print loss and epoch info if _ %(1_000) ==0: print(f'Epoch={_}\tloss={value:.3e}')

🔢 More

More compact boilerplate

# more compact definition 
# with class definition at runtime call
@pytc.treeclass
class StackedLinear2:

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

    def __call__(self,x):
        # The Linear layers are defined on the first call
        # and retrieved on the subsequent calls
        # this pattern is useful if module definition depends runtime data.
        
        in_dim = out_dim = x.shape[-1]
        k1,k2,k3 = self.keys

        x = self.register_node(Linear(k1,in_dim,10),name="l1")(x)
        x = jax.nn.tanh(x)
        x = self.register_node(Linear(k2,10,10),name="l2")(x)
        x = jax.nn.tanh(x)
        x = self.register_node(Linear(k3,10,out_dim),name="l3")(x)

        return x

Using out-of-place indexing on Pytrees

Similar to JAX pytreeclass provides .at property for out-of-place update.

# get layer1
layer1 = model.l1

# layer1 repr
>>> print(f"{layer1!r}")
Linear(
  weight=f32[1,10],
  bias=f32[1,10])

# layer1 str
>>> print(f"{layer1!s}")
Linear(
  weight=
    [[-2.5491788   1.674097    0.07813213  0.47670904 -1.8760327  -0.9941608
       0.2808009   0.6522513  -0.53470623  1.0796958 ]],
  bias=
    [[1.0368661  0.98985153 1.0104426  0.9997676  1.2349331  0.9800282
      0.9618377  0.99291945 0.9431369  1.0172408 ]])

# set negative values to 0
>>> print(layer1.at[layer1<0].set(0))
Linear(
  weight=
    [[0.         1.674097   0.07813213 0.47670904 0.         0.
      0.2808009  0.6522513  0.         1.0796958 ]],
  bias=
    [[1.0368661  0.98985153 1.0104426  0.9997676  1.2349331  0.9800282
      0.9618377  0.99291945 0.9431369  1.0172408 ]])

# get only positive values
>>> print(layer1.at[layer1>0].get())
Linear(
  weight=
    [1.674097   0.07813213 0.47670904 0.2808009  0.6522513  1.0796958 ],
  bias=
    [1.0368661  0.98985153 1.0104426  0.9997676  1.2349331  0.9800282
     0.9618377  0.99291945 0.9431369  1.0172408 ])

Perform Math operations on Pytrees

@pytc.treeclass
class Test :
    a : float
    b : float
    c : float
    name : str 

# basic operations
>>> A = Test(10,20,30,'A')
>>> (A + A)                 # Test(20,40,60,'A')
>>> (A - A)                 # Test(0,0,0,'A')
>>> (A*A).reduce_mean()     # 1400
>>> (A + 1)                 # Test(11,21,31,'A')

# only add 1 to field `a`
# all other fields are set to None and returns the same class
>>> assert (A['a'] + 1) == Test(11,None,None,'A')

# use `|` to merge classes by performing ( left_node or  right_node )
>>> Aa = A['a'] + 10 # Test(a=20,b=None,c=None,name=A)
>>> Ab = A['b'] + 10 # Test(a=None,b=30,c=None,name=A)

>>> assert (Aa | Ab | A ) == Test(20,30,30,'A')

# indexing by class
>>> A[A>10]  # Test(a=None,b=20,c=30,name='A')

# Register custom operations
>>> B = Test([10,10],20,30,'B')
>>> B.register_op( func=lambda node:node+1,name='plus_one')
>>> B.plus_one()  # Test(a=[11, 11],b=21,c=31,name='B')


# Register custom reduce operations ( similar to functools.reduce)
>>> C = Test(jnp.array([10,10]),20,30,'C')

>>> C.register_op(
        func=jnp.prod,            # function applied on each node
        name='product',           # name of the function
        reduce_op=lambda x,y:x*y, # function applied between nodes (accumulated * current node)
        init_val=1                # initializer for the reduce function
                )

# product applies only on each node
# and returns an instance of the same class
>>> C.product() # Test(a=100,b=20,c=30,name='C')

# `reduce_` + name of the registered function (`product`)
# reduces the class and returns a value
>>> C.reduce_product() # 60000

📝 Applications

Description
Physics informed neural network (PINN)

📙 Acknowledgements

• Equinox
• Treex
• tree-math