JAX Frequently Asked Questions (FAQ)

We are collecting here answers to frequently asked questions. Contributions welcome!

jit changes the behavior of my function

If you have a Python function that changes behavior after using jax.jit(), perhaps your function uses global state, or has side-effects. In the following code, the impure_func uses the global y and has a side-effect due to print:

y = 0

# @jit   # Different behavior with jit
def impure_func(x):
  print("Inside:", y)
  return x + y

for y in range(3):
  print("Result:", impure_func(y))

Without jit the output is:

Inside: 0
Result: 0
Inside: 1
Result: 2
Inside: 2
Result: 4

and with jit it is:

Inside: 0
Result: 0
Result: 1
Result: 2

For jax.jit(), the function is executed once using the Python interpreter, at which time the Inside printing happens, and the first value of y is observed. Then, the function is compiled and cached, and executed multiple times with different values of x, but with the same first value of y.

Additional reading:

jit decorated function is very slow to compile

If your jit decorated function takes tens of seconds (or more!) to run the first time you call it, but executes quickly when called again, JAX is taking a long time to trace or compile your code.

This is usually a symptom of calling your function generating a large amount of code in JAX’s internal representation, typically because it makes heavy use of Python control flow such as for loop. For a handful of loop iterations Python is OK, but if you need _many_ loop iterations, you should rewrite your code to make use of JAX’s structured control flow primitives (such as lax.scan()) or avoid wrapping the loop with jit (you can still use jit decorated functions inside the loop).

If you’re not sure if this is the problem, you can try running jax.make_jaxpr() on your function. You can expect slow compilation if the output is many hundreds or thousands of lines long.

Sometimes it isn’t obvious how to rewrite your code to avoid Python loops because your code makes use of many arrays with different shapes. The recommended solution in this case is to make use of functions like jax.numpy.where() to do your computation on padded arrays with fixed shape. The JAX team is exploring a “masking” transformation to make such code easier to write.

If your functions are slow to compile for another reason, please open an issue on GitHub.

Controlling data and computation placement on devices

Let’s first look at the principles of data and computation placement in JAX.

In JAX, the computation follows data placement. JAX arrays have two placement properties: 1) the device where the data resides; and 2) whether it is committed to the device or not (the data is sometimes referred to as being sticky to the device).

By default, JAX arrays are placed uncommitted on the default device (jax.devices()[0]), which is the first GPU by default. If no GPU is present, jax.devices()[0] is the first CPU. The default device can be set to “cpu” or “gpu” manually by setting the environment variable JAX_PLATFORM_NAME or the absl flag --jax_platform_name.

>>> from jax import numpy as jnp
>>> print(jnp.ones(3).device_buffer.device())  

Computations involving uncommitted data are performed on the default device and the results are uncommitted on the default device.

Data can also be placed explicitly on a device using jax.device_put() with a device parameter, in which case the data becomes committed to the device:

>>> import jax
>>> from jax import device_put
>>> print(device_put(1, jax.devices()[2]).device_buffer.device())  

Computations involving some committed inputs will happen on the committed device and the result will be committed on the same device. Invoking an operation on arguments that are committed to more than one device will raise an error.

You can also use jax.device_put() without a device parameter. If the data is already on a device (committed or not), it’s left as-is. If the data isn’t on any device—that is, it’s a regular Python or NumPy value—it’s placed uncommitted on the default device.

Jitted functions behave like any other primitive operations—they will follow the data and will show errors if invoked on data committed on more than one device.

jnp.device_put(jnp.zeros(...), jax.devices()[1]) or similar will actually create the array of zeros on jax.devices()[1], instead of creating the array on the default device then moving it. This is thanks to some laziness in array creation, which holds for all the constant creation operations (ones, full, eye, etc).

(As of April 2020, jax.jit() has a device parameter that affects the device placement. That parameter is experimental, is likely to be removed or changed, and its use is not recommended.)

For a worked-out example, we recommend reading through test_computation_follows_data in multi_device_test.py.

Benchmarking JAX code

You just ported a tricky function from NumPy/SciPy to JAX. Did that actuallly speed things up?

Keep in mind these important differences from NumPy when measuring the speed of code using JAX:

  1. JAX code is Just-In-Time (JIT) compiled. Most code written in JAX can be written in such a way that it supports JIT compilation, which can make it run much faster (see To JIT or not to JIT). To get maximium performance from JAX, you should apply jax.jit() on your outer-most function calls.

    Keep in mind that the first time you run JAX code, it will be slower because it is being compiled. This is true even if you don’t use jit in your own code, because JAX’s builtin functions are also JIT compiled.

  2. JAX has asynchronous dispatch. This means that you need to call .block_until_ready() to ensure that computation has actually happened (see Asynchronous dispatch).

  3. JAX by default only uses 32-bit dtypes. You may want to either explicitly use 32-bit dtypes in NumPy or enable 64-bit dtypes in JAX (see Double (64 bit) precision) for a fair comparison.

  4. Transferring data between CPUs and accelerators takes time. If you only want to measure the how long it takes to evaluate a function, you may want to transfer data to the device on which you want to run it first (see Controlling data and computation placement on devices).

Here’s an example of how to put together all these tricks into a microbenchmark for comparing JAX versus NumPy, making using of IPython’s convenient %time and %timeit magics:

import numpy as np
import jax.numpy as jnp
import jax

def f(x):  # function we're benchmarking (works in both NumPy & JAX)
  return x.T @ (x - x.mean(axis=0))

x_np = np.ones((1000, 1000), dtype=np.float32)  # same as JAX default dtype
%timeit f(x_np)  # measure NumPy runtime

%time x_jax = jax.device_put(x_np)  # measure JAX device transfer time
f_jit = jax.jit(f)
%time f_jit(x_jax).block_until_ready()  # measure JAX compilation time
%timeit f_jit(x_jax).block_until_ready()  # measure JAX runtime

When run with a GPU in Colab, we see:

  • NumPy takes 16.2 ms per evaluation on the CPU

  • JAX takes 1.26 ms to copy the NumPy arrays onto the GPU

  • JAX takes 193 ms to compile the function

  • JAX takes 485 µs per evaluation on the GPU

In this case, we see that once the data is transfered and the function is compiled, JAX on the GPU is about 30x faster for repeated evaluations.

Is this a fair comparison? Maybe. The performance that ultimately matters is for running full applications, which inevitably include some amount of both data transfer and compilation. Also, we were careful to pick large enough arrays (1000x1000) and an intensive enough computation (the @ operator is performing matrix-matrix multiplication) to amortize the increased overhead of JAX/accelerators vs NumPy/CPU. For example, if switch this example to use 10x10 input instead, JAX/GPU runs 10x slower than NumPy/CPU (100 µs vs 10 µs).

Abstract tracer value encountered where concrete value is expected error

See jax.errors.ConcretizationTypeError

Different kinds of JAX values

In the process of transforming functions, JAX replaces some function arguments with special tracer values.

You could see this if you use a print statement:

def func(x):
  return np.cos(x)

res = jax.jit(func)(0.)

The above code does return the correct value 1. but it also prints Traced<ShapedArray(float32[])> for the value of x. Normally, JAX handles these tracer values internally in a transparent way, e.g., in the numeric JAX primitives that are used to implement the jax.numpy functions. This is why np.cos works in the example above.

More precisely, a tracer value is introduced for the argument of a JAX-transformed function, except the arguments identified by special parameters such as static_argnums for jax.jit() or static_broadcasted_argnums for jax.pmap(). Typically, computations that involve at least a tracer value will produce a tracer value. Besides tracer values, there are regular Python values: values that are computed outside JAX transformations, or arise from above-mentioned static arguments of certain JAX transformations, or computed solely from other regular Python values. These are the values that are used everywhere in absence of JAX transformations.

A tracer value carries an abstract value, e.g., ShapedArray with information about the shape and dtype of an array. We will refer here to such tracers as abstract tracers. Some tracers, e.g., those that are introduced for arguments of autodiff transformations, carry ConcreteArray abstract values that actually include the regular array data, and are used, e.g., for resolving conditionals. We will refer here to such tracers as concrete tracers. Tracer values computed from these concrete tracers, perhaps in combination with regular values, result in concrete tracers. A concrete value is either a regular value or a concrete tracer.

Most often values computed from tracer values are themselves tracer values. There are very few exceptions, when a computation can be entirely done using the abstract value carried by a tracer, in which case the result can be a regular value. For example, getting the shape of a tracer with ShapedArray abstract value. Another example is when explicitly casting a concrete tracer value to a regular type, e.g., int(x) or x.astype(float). Another such situation is for bool(x), which produces a Python bool when concreteness makes it possible. That case is especially salient because of how often it arises in control flow.

Here is how the transformations introduce abstract or concrete tracers:

  • jax.jit(): introduces abstract tracers for all positional arguments except those denoted by static_argnums, which remain regular values.

  • jax.pmap(): introduces abstract tracers for all positional arguments except those denoted by static_broadcasted_argnums.

  • jax.vmap(), jax.make_jaxpr(), xla_computation(): introduce abstract tracers for all positional arguments.

  • jax.jvp() and jax.grad() introduce concrete tracers for all positional arguments. An exception is when these transformations are within an outer transformation and the actual arguments are themselves abstract tracers; in that case, the tracers introduced by the autodiff transformations are also abstract tracers.

  • All higher-order control-flow primitives (lax.cond(), lax.while_loop(), lax.fori_loop(), lax.scan()) when they process the functionals introduce abstract tracers, whether or not there is a JAX transformation in progress.

All of this is relevant when you have code that can operate only on regular Python values, such as code that has conditional control-flow based on data:

def divide(x, y):
  return x / y if y >= 1. else 0.

If we want to apply jax.jit(), we must ensure to specify static_argnums=1 to ensure y stays a regular value. This is due to the boolean expression y >= 1., which requires concrete values (regular or tracers). The same would happen if we write explicitly bool(y >= 1.), or int(y), or float(y).

Interestingly, jax.grad(divide)(3., 2.), works because jax.grad() uses concrete tracers, and resolves the conditional using the concrete value of y.

Gradients contain NaN where using where

If you define a function using where to avoid an undefined value, if you are not careful you may obtain a NaN for reverse differentiation:

def my_log(x):
  return np.where(x > 0., np.log(x), 0.)

my_log(0.) ==> 0.  # Ok
jax.grad(my_log)(0.)  ==> NaN

A short explanation is that during grad computation the adjoint corresponding to the undefined np.log(x) is a NaN and when it gets accumulated to the adjoint of the np.where. The correct way to write such functions is to ensure that there is a np.where inside the partially-defined function, to ensure that the adjoint is always finite:

def safe_for_grad_log(x):
  return np.log(np.where(x > 0., x, 1.))

safe_for_grad_log(0.) ==> 0.  # Ok
jax.grad(safe_for_grad_log)(0.)  ==> 0.  # Ok

The inner np.where may be needed in addition to the original one, e.g.:

def my_log_or_y(x, y):
  """Return log(x) if x > 0 or y"""
  return np.where(x > 0., np.log(np.where(x > 0., x, 1.), y)

Additional reading: