JAX Frequently Asked Questions (FAQ)#

We are collecting answers to frequently asked questions here. 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 changes the exact numerics of outputs#

Sometimes users are surprised by the fact that wrapping a function with jit() can change the function’s outputs. For example:

>>> from jax import jit
>>> import jax.numpy as jnp
>>> def f(x):
...   return jnp.log(jnp.sqrt(x))
>>> x = jnp.pi
>>> print(f(x))
>>> print(jit(f)(x))

This slight difference in output comes from optimizations within the XLA compiler: during compilation, XLA will sometimes rearrange or elide certain operations to make the overall computation more efficient.

In this case, XLA utilizes the properties of the logarithm to replace log(sqrt(x)) with 0.5 * log(x), which is a mathematically identical expression that can be computed more efficiently than the original. The difference in output comes from the fact that floating point arithmetic is only a close approximation of real math, so different ways of computing the same expression may have subtly different results.

Other times, XLA’s optimizations may lead to even more drastic differences. Consider the following example:

>>> def f(x):
...   return jnp.log(jnp.exp(x))
>>> x = 100.0
>>> print(f(x))
>>> print(jit(f)(x))

In non-JIT-compiled op-by-op mode, the result is inf because jnp.exp(x) overflows and returns inf. Under JIT, however, XLA recognizes that log is the inverse of exp, and removes the operations from the compiled function, simply returning the input. In this case, JIT compilation produces a more accurate floating point approximation of the real result.

Unfortunately the full list of XLA’s algebraic simplifications is not well documented, but if you’re familiar with C++ and curious about what types of optimizations the XLA compiler makes, you can see them in the source code: algebraic_simplifier.cc.

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 sign that calling your function generates a large amount of code in JAX’s internal representation, typically because it makes heavy use of Python control flow such as for loops. 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.

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

How to use jit with methods?#

Most examples of jax.jit() concern decorating stand-alone Python functions, but decorating a method within a class introduces some complication. For example, consider the following simple class, where we’ve used a standard jit() annotation on a method:

>>> import jax.numpy as jnp
>>> from jax import jit

>>> class CustomClass:
...   def __init__(self, x: jnp.ndarray, mul: bool):
...     self.x = x
...     self.mul = mul
...   @jit  # <---- How to do this correctly?
...   def calc(self, y):
...     if self.mul:
...       return self.x * y
...     return y

However, this approach will result in an error when you attempt to call this method:

>>> c = CustomClass(2, True)
>>> c.calc(3)  
TypeError                                 Traceback (most recent call last)
  File "<stdin>", line 1, in <module
TypeError: Argument '<CustomClass object at 0x7f7dd4125890>' of type <class 'CustomClass'> is not a valid JAX type.

The problem is that the first argument to the function is self, which has type CustomClass, and JAX does not know how to handle this type. There are three basic strategies we might use in this case, and we’ll discuss them below.

Strategy 1: JIT-compiled helper function#

The most straightforward approach is to create a helper function external to the class that can be JIT-decorated in the normal way. For example:

>>> from functools import partial

>>> class CustomClass:
...   def __init__(self, x: jnp.ndarray, mul: bool):
...     self.x = x
...     self.mul = mul
...   def calc(self, y):
...     return _calc(self.mul, self.x, y)

>>> @partial(jit, static_argnums=0)
... def _calc(mul, x, y):
...   if mul:
...     return x * y
...   return y

The result will work as expected:

>>> c = CustomClass(2, True)
>>> print(c.calc(3))

The benefit of such an approach is that it is simple, explicit, and it avoids the need to teach JAX how to handle objects of type CustomClass. However, you may wish to keep all the method logic in the same place.

Strategy 2: Marking self as static#

Another common pattern is to use static_argnums to mark the self argument as static. But this must be done with care to avoid unexpected results. You may be tempted to simply do this:

>>> class CustomClass:
...   def __init__(self, x: jnp.ndarray, mul: bool):
...     self.x = x
...     self.mul = mul
...   # WARNING: this example is broken, as we'll see below. Don't copy & paste!
...   @partial(jit, static_argnums=0)
...   def calc(self, y):
...     if self.mul:
...       return self.x * y
...     return y

If you call the method, it will no longer raise an error:

>>> c = CustomClass(2, True)
>>> print(c.calc(3))

However, there is a catch: if you mutate the object after the first method call, the subsequent method call may return an incorrect result:

>>> c.mul = False
>>> print(c.calc(3))  # Should print 3

Why is this? When you mark an object as static, it will effectively be used as a dictionary key in JIT’s internal compilation cache, meaning its hash (i.e. hash(obj)) equality (i.e. obj1 == obj2) and object identity (i.e. obj1 is obj2) will be assumed to have consistent behavior. The default __hash__ for a custom object is its object ID, and so JAX has no way of knowing that a mutated object should trigger a re-compilation.

You can partially address this by defining an appropriate __hash__ and __eq__ methods for your object; for example:

>>> class CustomClass:
...   def __init__(self, x: jnp.ndarray, mul: bool):
...     self.x = x
...     self.mul = mul
...   @partial(jit, static_argnums=0)
...   def calc(self, y):
...     if self.mul:
...       return self.x * y
...     return y
...   def __hash__(self):
...     return hash((self.x, self.mul))
...   def __eq__(self, other):
...     return (isinstance(other, CustomClass) and
...             (self.x, self.mul) == (other.x, other.mul))

(see the object.__hash__() documentation for more discussion of the requirements when overriding __hash__).

This should work correctly with JIT and other transforms so long as you never mutate your object. Mutations of objects used as hash keys lead to several subtle problems, which is why for example mutable Python containers (e.g. dict, list) don’t define __hash__, while their immutable counterparts (e.g. tuple) do.

If your class relies on in-place mutations (such as setting self.attr = ... within its methods), then your object is not really “static” and marking it as such may lead to problems. Fortunately, there’s another option for this case.

Strategy 3: Making CustomClass a PyTree#

The most flexible approach to correctly JIT-compiling a class method is to register the type as a custom PyTree object; see Extending pytrees. This lets you specify exactly which components of the class should be treated as static and which should be treated as dynamic. Here’s how it might look:

>>> class CustomClass:
...   def __init__(self, x: jnp.ndarray, mul: bool):
...     self.x = x
...     self.mul = mul
...   @jit
...   def calc(self, y):
...     if self.mul:
...       return self.x * y
...     return y
...   def _tree_flatten(self):
...     children = (self.x,)  # arrays / dynamic values
...     aux_data = {'mul': self.mul}  # static values
...     return (children, aux_data)
...   @classmethod
...   def _tree_unflatten(cls, aux_data, children):
...     return cls(*children, **aux_data)

>>> from jax import tree_util
>>> tree_util.register_pytree_node(CustomClass,
...                                CustomClass._tree_flatten,
...                                CustomClass._tree_unflatten)

This is certainly more involved, but it solves all the issues associated with the simpler approaches used above:

>>> c = CustomClass(2, True)
>>> print(c.calc(3))

>>> c.mul = False  # mutation is detected
>>> print(c.calc(3))

>>> c = CustomClass(jnp.array(2), True)  # non-hashable x is supported
>>> print(c.calc(3))

So long as your tree_flatten and tree_unflatten functions correctly handle all relevant attributes in the class, you should be able to use objects of this type directly as arguments to JIT-compiled functions, without any special annotations.

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 or TPU by default. If no GPU or TPU is present, jax.devices()[0] is the CPU. The default device can be temporarily overridden with the jax.default_device() context manager, or set for the whole process by setting the environment variable JAX_PLATFORMS or the absl flag --jax_platforms to “cpu”, “gpu”, or “tpu” (JAX_PLATFORMS can also be a list of platforms, which determines which platforms are available in priority order).

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

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
>>> arr = device_put(1, jax.devices()[2])  
>>> print(arr.devices())  

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.

(Before PR #6002 in March 2021 there was some laziness in creation of array constants, so that jax.device_put(jnp.zeros(...), jax.devices()[1]) or similar would actually create the array of zeros on jax.devices()[1], instead of creating the array on the default device then moving it. But this optimization was removed so as to simplify the implementation.)

(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 actually 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 maximum 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 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 transferred 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 we switch this example to use 10x10 input instead, JAX/GPU runs 10x slower than NumPy/CPU (100 µs vs 10 µs).

Is JAX faster than NumPy?#

One question users frequently attempt to answer with such benchmarks is whether JAX is faster than NumPy; due to the difference in the two packages, there is not a simple answer.

Broadly speaking:

  • NumPy operations are executed eagerly, synchronously, and only on CPU.

  • JAX operations may be executed eagerly or after compilation (if inside jit()); they are dispatched asynchronously (see Asynchronous dispatch); and they can be executed on CPU, GPU, or TPU, each of which have vastly different and continuously evolving performance characteristics.

These architectural differences make meaningful direct benchmark comparisons between NumPy and JAX difficult.

Additionally, these differences have led to different engineering focus between the packages: for example, NumPy has put significant effort into decreasing the per-call dispatch overhead for individual array operations, because in NumPy’s computational model that overhead cannot be avoided. JAX, on the other hand, has several ways to avoid dispatch overhead (e.g. JIT compilation, asynchronous dispatch, batching transforms, etc.), and so reducing per-call overhead has been less of a priority.

Keeping all that in mind, in summary: if you’re doing microbenchmarks of individual array operations on CPU, you can generally expect NumPy to outperform JAX due to its lower per-operation dispatch overhead. If you’re running your code on GPU or TPU, or are benchmarking more complicated JIT-compiled sequences of operations on CPU, you can generally expect JAX to outperform NumPy.

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 jnp.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 jnp.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.

Buffer donation#

When JAX executes a computation it uses buffers on the device for all inputs and outputs. If you know that one of the inputs is not needed after the computation, and if it matches the shape and element type of one of the outputs, you can specify that you want the corresponding input buffer to be donated to hold an output. This will reduce the memory required for the execution by the size of the donated buffer.

If you have something like the following pattern, you can use buffer donation:

params, state = jax.pmap(update_fn, donate_argnums=(0, 1))(params, state)

You can think of this as a way to do a memory-efficient functional update on your immutable JAX arrays. Within the boundaries of a computation XLA can make this optimization for you, but at the jit/pmap boundary you need to guarantee to XLA that you will not use the donated input buffer after calling the donating function.

You achieve this by using the donate_argnums parameter to the functions jax.jit(), jax.pjit(), and jax.pmap(). This parameter is a sequence of indices (0 based) into the positional argument list:

def add(x, y):
  return x + y

x = jax.device_put(np.ones((2, 3)))
y = jax.device_put(np.ones((2, 3)))
# Execute `add` with donation of the buffer for `y`. The result has
# the same shape and type as `y`, so it will share its buffer.
z = jax.jit(add, donate_argnums=(1,))(x, y)

Note that this currently does not work when calling your function with key-word arguments! The following code will not donate any buffers:

params, state = jax.pmap(update_fn, donate_argnums=(0, 1))(params=params, state=state)

If an argument whose buffer is donated is a pytree, then all the buffers for its components are donated:

def add_ones(xs: List[Array]):
  return [x + 1 for x in xs]

xs = [jax.device_put(np.ones((2, 3))), jax.device_put(np.ones((3, 4)))]
# Execute `add_ones` with donation of all the buffers for `xs`.
# The outputs have the same shape and type as the elements of `xs`,
# so they will share those buffers.
z = jax.jit(add_ones, donate_argnums=0)(xs)

It is not allowed to donate a buffer that is used subsequently in the computation, and JAX will give an error because the buffer for y has become invalid after it was donated:

# Donate the buffer for `y`
z = jax.jit(add, donate_argnums=(1,))(x, y)
w = y + 1  # Reuses `y` whose buffer was donated above
# >> RuntimeError: Invalid argument: CopyToHostAsync() called on invalid buffer

You will get a warning if the donated buffer is not used, e.g., because there are more donated buffers than can be used for the outputs:

# Execute `add` with donation of the buffers for both `x` and `y`.
# One of those buffers will be used for the result, but the other will
# not be used.
z = jax.jit(add, donate_argnums=(0, 1))(x, y)
# >> UserWarning: Some donated buffers were not usable: f32[2,3]{1,0}

The donation may also be unused if there is no output whose shape matches the donation:

y = jax.device_put(np.ones((1, 3)))  # `y` has different shape than the output
# Execute `add` with donation of the buffer for `y`.
z = jax.jit(add, donate_argnums=(1,))(x, y)
# >> UserWarning: Some donated buffers were not usable: f32[1,3]{1,0}

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 jnp.where(x > 0., jnp.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 jnp.log(x) is a NaN and it gets accumulated to the adjoint of the jnp.where. The correct way to write such functions is to ensure that there is a jnp.where inside the partially-defined function, to ensure that the adjoint is always finite:

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

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

The inner jnp.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 jnp.where(x > 0., jnp.log(jnp.where(x > 0., x, 1.)), y)

Additional reading:

Why are gradients zero for functions based on sort order?#

If you define a function that processes the input using operations that depend on the relative ordering of inputs (e.g. max, greater, argsort, etc.) then you may be surprised to find that the gradient is everywhere zero. Here is an example, where we define f(x) to be a step function that returns 0 when x is negative, and 1 when x is positive:

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

def f(x):
  return (x > 0).astype(float)

df = jax.vmap(jax.grad(f))

x = jnp.array([-1.0, -0.5, 0.0, 0.5, 1.0])

print(f"f(x)  = {f(x)}")
# f(x)  = [0. 0. 0. 1. 1.]

print(f"df(x) = {df(x)}")
# df(x) = [0. 0. 0. 0. 0.]

The fact that the gradient is everywhere zero may be confusing at first glance: after all, the output does change in response to the input, so how can the gradient be zero? However, zero turns out to be the correct result in this case.

Why is this? Remember that what differentiation is measuring the change in f given an infinitesimal change in x. For x=1.0, f returns 1.0. If we perturb x to make it slightly larger or smaller, this does not change the output, so by definition, grad(f)(1.0) should be zero. This same logic holds for all values of f greater than zero: infinitesimally perturbing the input does not change the output, so the gradient is zero. Similarly, for all values of x less than zero, the output is zero. Perturbing x does not change this output, so the gradient is zero. That leaves us with the tricky case of x=0. Surely, if you perturb x upward, it will change the output, but this is problematic: an infinitesimal change in x produces a finite change in the function value, which implies the gradient is undefined. Fortunately, there’s another way for us to measure the gradient in this case: we perturb the function downward, in which case the output does not change, and so the gradient is zero. JAX and other autodiff systems tend to handle discontinuities in this way: if the positive gradient and negative gradient disagree, but one is defined and the other is not, we use the one that is defined. Under this definition of the gradient, mathematically and numerically the gradient of this function is everywhere zero.

The problem stems from the fact that our function has a discontinuity at x = 0. Our f here is essentially a Heaviside Step Function, and we can use a Sigmoid Function as a smoothed replacement. The sigmoid is approximately equal to the heaviside function when x is far from zero, but replaces the discontinuity at x = 0 with a smooth, differentiable curve. As a result of using jax.nn.sigmoid(), we get a similar computation with well-defined gradients:

def g(x):
  return jax.nn.sigmoid(x)

dg = jax.vmap(jax.grad(g))

x = jnp.array([-10.0, -1.0, 0.0, 1.0, 10.0])

with np.printoptions(suppress=True, precision=2):
  print(f"g(x)  = {g(x)}")
  # g(x)  = [0.   0.27 0.5  0.73 1.  ]

  print(f"dg(x) = {dg(x)}")
  # dg(x) = [0.   0.2  0.25 0.2  0.  ]

The jax.nn submodule also has smooth versions of other common rank-based functions, for example jax.nn.softmax() can replace uses of jax.numpy.argmax(), jax.nn.soft_sign() can replace uses of jax.numpy.sign(), jax.nn.softplus() or jax.nn.squareplus() can replace uses of jax.nn.relu(), etc.

How can I convert a JAX Tracer to a NumPy array?#

When inspecting a transformed JAX function at runtime, you’ll find that array values are replaced by Tracer objects:

def f(x):
  return x


This prints the following:

<class 'jax.interpreters.partial_eval.DynamicJaxprTracer'>

A frequent question is how such a tracer can be converted back to a normal NumPy array. In short, it is impossible to convert a Tracer to a NumPy array, because a tracer is an abstract representation of every possible value with a given shape and dtype, while a numpy array is a concrete member of that abstract class. For more discussion of how tracers work within the context of JAX transformations, see JIT mechanics.

The question of converting Tracers back to arrays usually comes up within the context of another goal, related to accessing intermediate values in a computation at runtime. For example:

  • If you wish to print a traced value at runtime for debugging purposes, you might consider using jax.debug.print().

  • If you wish to call non-JAX code within a transformed JAX function, you might consider using jax.pure_callback(), an example of which is available at Pure callback example.

  • If you wish to input or output array buffers at runtime (for example, load data from file, or log the contents of the array to disk), you might consider using jax.experimental.io_callback(), an example of which can be found at IO callback example.

For more information on runtime callbacks and examples of their use, see External callbacks in JAX.

Why do some CUDA libraries fail to load/initialize?#

When resolving dynamic libraries, JAX uses the usual dynamic linker search pattern. JAX sets RPATH to point to the JAX-relative location of the pip-installed NVIDIA CUDA packages, preferring them if installed. If ld.so cannot find your CUDA runtime libraries along its usual search path, then you must include the paths to those libraries explicitly in LD_LIBRARY_PATH. The easiest way to ensure your CUDA files are discoverable is to simply install the nvidia-*-cu12 pip packages, which are included in the standard jax[cuda_12] install option.

Occasionally, even when you have ensured that your runtime libraries are discoverable, there may still be some issues with loading or initializing them. A common cause of such issues is simply having insufficient memory for CUDA library initialization at runtime. This sometimes occurs because JAX will pre-allocate too large of a chunk of currently available device memory for faster execution, occasionally resulting in insufficient memory being left available for runtime CUDA library initialization.

This is especially likely when running multiple JAX instances, running JAX in tandem with TensorFlow which performs its own pre-allocation, or when running JAX on a system where the GPU is being heavily utilized by other processes. When in doubt, try running the program again with reduced pre-allocation, either by reducing XLA_PYTHON_CLIENT_MEM_FRACTION from the default of .75, or setting XLA_PYTHON_CLIENT_PREALLOCATE=false. For more details, please see the page on JAX GPU memory allocation.