pytreeclass 0.9.2

Description:

pytreeclass 0.9.2

Installation
|Description
|Quick Example
|StatefulComputation
|Benchamrks
|Acknowledgements













🛠️ Installation
pip install pytreeclass

Install development version
pip install git+https://github.com/ASEM000/pytreeclass

📖 Description
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 and 🍳 Common recipes to check if this library is a good fit for your work. If you find the package useful consider giving it a 🌟.
⏩ Quick Example





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
# ├── .a=100.0
# ├── .b:tuple
# │ ├── [0]=10.0
# │ └── [1]=3.0
# └── .c=f32[3](μ=36.33, σ=45.02, ∈[4.00,100.00])

print(tc.tree_summary(tree))
# ┌─────┬──────┬─────┬──────┐
# │Name │Type │Count│Size │
# ├─────┼──────┼─────┼──────┤
# │.a │float │1 │ │
# ├─────┼──────┼─────┼──────┤
# │.b[0]│float │1 │ │
# ├─────┼──────┼─────┼──────┤
# │.b[1]│float │1 │ │
# ├─────┼──────┼─────┼──────┤
# │.c │f32[3]│3 │12.00B│
# ├─────┼──────┼─────┼──────┤
# │Σ │Tree │6 │12.00B│
# └─────┴──────┴─────┴──────┘

# ** 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.])





📜 Stateful computations
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 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





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)





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.





@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






➕ Benchmarks

Benchmark flatten/unflatten compared to Flax and Equinox


CPUGPU






Benchmark simple training against `flax` and `equinox`
Training simple sequential linear benchmark against flax and equinox


Num of layers
Flax/tc time
Equinox/tc time


10
1.427
6.671


100
1.1130
2.714



📙 Acknowledgements

Lenses
Treex, Equinox, tree-math, Flax PyTreeNode, TensorFlow, PyTorch
Lovely JAX