Stateful Computations in JAX
Stateful Computations in JAX#
Authors: Vladimir Mikulik
This section explores how JAX constrains the implementation of stateful programs.
In machine learning, program state most often comes in the form of:
optimizer state, and
stateful layers, such as BatchNorm.
Some JAX transformations, most notably
jax.jit, impose constraints on the functions they transform. In particular, the function transformed by
jax.jit must have no side-effects. This is because any such side-effects will only be executed once, when the python version of the function is run during compilation. These side-effects will not be executed by the compiled function on subsequent runs.
Changing program state is one kind of side-effect. So, if we can’t have side effects, how do we update model parameters, the optimizer state, and use stateful layers in our models? This colab will explain this in detail, but the short answer is: with functional programming.
A simple example: Counter#
Let’s start by looking at a simple stateful program: a counter.
import jax import jax.numpy as jnp class Counter: """A simple counter.""" def __init__(self): self.n = 0 def count(self) -> int: """Increments the counter and returns the new value.""" self.n += 1 return self.n def reset(self): """Resets the counter to zero.""" self.n = 0 counter = Counter() for _ in range(3): print(counter.count())
1 2 3
n attribute maintains the counter’s state between successive calls of
count. It is modified as a side effect of calling
Let’s say we want to count fast, so we
count method. (In this example, this wouldn’t actually help speed anyway, for many reasons, but treat this as a toy model of wanting to JIT-compile the update of model parameters, where
jax.jit makes an enormous difference).
counter.reset() fast_count = jax.jit(counter.count) for _ in range(3): print(fast_count())
1 1 1
Oh no! Our counter isn’t working. This is because the line
self.n += 1
count is only called once, when JAX compiles the method call. Moreover, since the return value doesn’t depend on the arguments to
count, once it returns the first 1, subsequent calls to
fast_count will always return 1. This won’t do. So, how do we fix it?
The solution: explicit state#
Part of the problem with our counter was that the returned value didn’t depend on the arguments, meaning a constant was “baked into” the compiled output. But it shouldn’t be a constant – it should depend on the state. Well, then why don’t we make the state into an argument?
from typing import Tuple CounterState = int class CounterV2: def count(self, n: CounterState) -> Tuple[int, CounterState]: # You could just return n+1, but here we separate its role as # the output and as the counter state for didactic purposes. return n+1, n+1 def reset(self) -> CounterState: return 0 counter = CounterV2() state = counter.reset() for _ in range(3): value, state = counter.count(state) print(value)
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In this new version of
Counter, we moved
n to be an argument of
count, and added another return value that represents the new, updated, state. To use this counter, we now need to keep track of the state explicitly. But in return, we can now safely
jax.jit this counter:
state = counter.reset() fast_count = jax.jit(counter.count) for _ in range(3): value, state = fast_count(state) print(value)
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A general strategy#
We can apply the same process to any stateful method to convert it into a stateless one. We took a class of the form
class StatefulClass state: State def stateful_method(*args, **kwargs) -> Output:
and turned it into a class of the form
class StatelessClass def stateless_method(state: State, *args, **kwargs) -> (Output, State):
This is a common functional programming pattern, and, essentially, is the way that state is handled in all JAX programs.
Notice that the need for a class becomes less clear once we have rewritten it this way. We could just keep
stateless_method, since the class is no longer doing any work. This is because, like the strategy we just applied, object-oriented programming (OOP) is a way to help programmers understand program state.
In our case, the
CounterV2 class is nothing more than a namespace bringing all the functions that use
CounterState into one location. Exercise for the reader: do you think it makes sense to keep it as a class?
Incidentally, you’ve already seen an example of this strategy in the JAX pseudo-randomness API,
jax.random, shown in the Random Numbers section. Unlike Numpy, which manages random state using stateful classes, JAX requires the programmer to work directly with the random generator state – the PRNGKey.
Simple worked example: Linear Regression#
Let’s apply this strategy to a simple machine learning model: linear regression via gradient descent.
Here, we only deal with one kind of state: the model parameters. But generally, you’ll see many kinds of state being threaded in and out of JAX functions, like optimizer state, layer statistics for batchnorm, and others.
The function to look at carefully is
from typing import NamedTuple class Params(NamedTuple): weight: jnp.ndarray bias: jnp.ndarray def init(rng) -> Params: """Returns the initial model params.""" weights_key, bias_key = jax.random.split(rng) weight = jax.random.normal(weights_key, ()) bias = jax.random.normal(bias_key, ()) return Params(weight, bias) def loss(params: Params, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray: """Computes the least squares error of the model's predictions on x against y.""" pred = params.weight * x + params.bias return jnp.mean((pred - y) ** 2) LEARNING_RATE = 0.005 @jax.jit def update(params: Params, x: jnp.ndarray, y: jnp.ndarray) -> Params: """Performs one SGD update step on params using the given data.""" grad = jax.grad(loss)(params, x, y) # If we were using Adam or another stateful optimizer, # we would also do something like # ``` # updates, new_optimizer_state = optimizer(grad, optimizer_state) # ``` # and then use `updates` instead of `grad` to actually update the params. # (And we'd include `new_optimizer_state` in the output, naturally.) new_params = jax.tree_map( lambda param, g: param - g * LEARNING_RATE, params, grad) return new_params
Notice that we manually pipe the params in and out of the update function.
import matplotlib.pyplot as plt rng = jax.random.PRNGKey(42) # Generate true data from y = w*x + b + noise true_w, true_b = 2, -1 x_rng, noise_rng = jax.random.split(rng) xs = jax.random.normal(x_rng, (128, 1)) noise = jax.random.normal(noise_rng, (128, 1)) * 0.5 ys = xs * true_w + true_b + noise # Fit regression params = init(rng) for _ in range(1000): params = update(params, xs, ys) plt.scatter(xs, ys) plt.plot(xs, params.weight * xs + params.bias, c='red', label='Model Prediction') plt.legend();
Taking it further#
The strategy described above is how any (jitted) JAX program must handle state.
Handling parameters manually seems fine if you’re dealing with two parameters, but what if it’s a neural net with dozens of layers? You might already be getting worried about two things:
Are we supposed to initialize them all manually, essentially repeating what we already write in the forward pass definition?
Are we supposed to pipe all these things around manually?
The details can be tricky to handle, but there are examples of libraries that take care of this for you. See JAX Neural Network Libraries for some examples.