Understanding Jaxprs

Updated: May 3, 2020 (for commit f1a46fe).

Conceptually, one can think of JAX transformations as first trace-specializing the Python function to be transformed into a small and well-behaved intermediate form that is then interpreted with transformation-specific interpretation rules. One of the reasons JAX can pack so much power into such a small software package is that it starts with a familiar and flexible programming interface (Python with NumPy) and it uses the actual Python interpreter to do most of the heavy lifting to distill the essence of the computation into a simple statically-typed expression language with limited higher-order features. That language is the jaxpr language.

Not all Python programs can be processed this way, but it turns out that many scientific computing and machine learning programs can.

Before we proceed, it is important to point out that not all JAX transformations literally materialize a jaxpr as described above; some, e.g., differentiation or batching, will apply transformations incrementally during tracing. Nevertheless, if one wants to understand how JAX works internally, or to make use of the result of JAX tracing, it is useful to understand jaxprs.

A jaxpr instance represents a function with one or more typed parameters (input variables) and one or more typed results. The results depend only on the input variables; there are no free variables captured from enclosing scopes. The inputs and outputs have types, which in JAX are represented as abstract values. There are two related representations in the code for jaxprs, jax.core.Jaxpr and jax.core.ClosedJaxpr. A jax.core.ClosedJaxpr represents a partially-applied jax.core.Jaxpr, and is what you obtain when you use jax.make_jaxpr() to inspect jaxprs. It has the following fields:

  • jaxpr: is a jax.core.Jaxpr representing the actual computation content of the function (described below).

  • consts is a list of constants.

The most interesting part of the ClosedJaxpr is the actual execution content, represented as a jax.core.Jaxpr as printed using the following grammar:

jaxpr ::= { lambda Var* ; Var+.
            let Eqn*
            in  [Expr+] }
  • The parameters of the jaxpr are shown as two lists of variables separated by ;. The first set of variables are the ones that have been introduced to stand for constants that have been hoisted out. These are called the constvars, and in a jax.core.ClosedJaxpr the consts field holds corresponding values. The second list of variables, called invars, correspond to the inputs of the traced Python function.

  • Eqn* is a list of equations, defining intermediate variables referring to intermediate expressions. Each equation defines one or more variables as the result of applying a primitive on some atomic expressions. Each equation uses only input variables and intermediate variables defined by previous equations.

  • Expr+: is a list of output atomic expressions (literals or variables) for the jaxpr.

Equations are printed as follows:

Eqn  ::= let Var+ = Primitive [ Param* ] Expr+
  • Var+ are one or more intermediate variables to be defined as the output of a primitive invocation (some primitives can return multiple values)

  • Expr+ are one or more atomic expressions, each either a variable or a literal constant. A special variable unitvar or literal unit, printed as *, represents a value that is not needed in the rest of the computation and has been elided. That is, units are just placeholders.

  • Param* are zero or more named parameters to the primitive, printed in square brackets. Each parameter is shown as Name = Value.

Most jaxpr primitives are first-order (they take just one or more Expr as arguments):

Primitive := add | sub | sin | mul | ...

The jaxpr primitives are documented in the jax.lax module.

For example, here is the jaxpr produced for the function func1 below

>>> from jax import make_jaxpr
>>> import jax.numpy as jnp
>>> def func1(first, second):
...    temp = first + jnp.sin(second) * 3.
...    return jnp.sum(temp)
>>> print(make_jaxpr(func1)(jnp.zeros(8), jnp.ones(8)))
{ lambda  ; a b.
  let c = sin b
      d = mul c 3.0
      e = add a d
      f = reduce_sum[ axes=(0,) ] e
  in (f,) }

Here there are no constvars, a and b are the input variables and they correspond respectively to first and second function parameters. The scalar literal 3.0 is kept inline. The reduce_sum primitive has named parameters axes and input_shape, in addition to the operand e.

Note that JAX traces through Python-level control-flow and higher-order functions when it extracts the jaxpr. This means that just because a Python program contains functions and control-flow, the resulting jaxpr does not have to contain control-flow or higher-order features. For example, when tracing the function func3 JAX will inline the call to inner and the conditional if second.shape[0] > 4, and will produce the same jaxpr as before

>>> def func2(inner, first, second):
...   temp = first + inner(second) * 3.
...   return jnp.sum(temp)
>>> def inner(second):
...   if second.shape[0] > 4:
...     return jnp.sin(second)
...   else:
...     assert False
>>> def func3(first, second):
...   return func2(inner, first, second)
>>> print(make_jaxpr(func3)(jnp.zeros(8), jnp.ones(8)))
{ lambda  ; a b.
  let c = sin b
      d = mul c 3.0
      e = add a d
      f = reduce_sum[ axes=(0,) ] e
  in (f,) }

Handling PyTrees

In jaxpr there are no tuple types; instead primitives take multiple inputs and produce multiple outputs. When processing a function that has structured inputs or outputs, JAX will flatten those and in jaxpr they will appear as lists of inputs and outputs. For more details, please see the documentation for PyTrees (notebooks/JAX_pytrees).

For example, the following code produces an identical jaxpr to what we saw before (with two input vars, one for each element of the input tuple)

>>> def func4(arg):  # Arg is a pair
...   temp = arg[0] + jnp.sin(arg[1]) * 3.
...   return jnp.sum(temp)
>>> print(make_jaxpr(func4)((jnp.zeros(8), jnp.ones(8))))
{ lambda  ; a b.
  let c = sin b
      d = mul c 3.0
      e = add a d
      f = reduce_sum[ axes=(0,) ] e
  in (f,) }

Constant Vars

Some values in jaxprs are constants, in that their value does not depend on the jaxpr’s arguments. When these values are scalars they are represented directly in the jaxpr equations; non-scalar array constants are instead hoisted out to the top-level jaxpr, where they correspond to constant variables (“constvars”). These constvars differ from the other jaxpr parameters (“invars”) only as a bookkeeping convention.

Higher-order primitives

jaxpr includes several higher-order primitives. They are more complicated because they include sub-jaxprs.


JAX traces through normal Python conditionals. To capture a conditional expression for dynamic execution, one must use the jax.lax.switch() and jax.lax.cond() constructors, which have the signatures:

lax.switch(index: int, branches: Sequence[A -> B], operand: A) -> B

lax.cond(pred: bool, true_body: A -> B, false_body: A -> B, operand: A) -> B

Both of these will bind a primitive called cond internally. The cond primitive in jaxprs reflects the more general signature of lax.switch(): it takes an integer denoting the index of the branch to execute (clamped into valid indexing range).

For example:

>>> from jax import lax
>>> def one_of_three(index, arg):
...   return lax.switch(index, [lambda x: x + 1.,
...                             lambda x: x - 2.,
...                             lambda x: x + 3.],
...                     arg)
>>> print(make_jaxpr(one_of_three)(1, 5.))
{ lambda  ; a b.
  let c = clamp 0 a 2
      d = cond[ branches=( { lambda  ; a.
                             let b = add a 1.0
                             in (b,) }
                           { lambda  ; a.
                             let b = sub a 2.0
                             in (b,) }
                           { lambda  ; a.
                             let b = add a 3.0
                             in (b,) } )
                linear=(False,) ] c b
  in (d,) }

The cond primitive has a number of parameters:

  • branches are jaxprs that correspond to the branch functionals. In this example, those functionals each take one input variable, corresponding to x.

  • linear is a tuple of booleans that is used internally by the auto-differentiation machinery to encode which of the input parameters are used linearly in the conditional.

The above instance of the cond primitive takes two operands. The first one (c) is the branch index, then b is the operand (arg) to be passed to whichever jaxpr in branches is selected by the branch index.

Another example, using lax.cond():

>>> from jax import lax
>>> def func7(arg):
...   return lax.cond(arg >= 0.,
...                   lambda xtrue: xtrue + 3.,
...                   lambda xfalse: xfalse - 3.,
...                   arg)
>>> print(make_jaxpr(func7)(5.))
{ lambda  ; a.
  let b = ge a 0.0
      c = convert_element_type[ new_dtype=int32
                                old_dtype=bool ] b
      d = cond[ branches=( { lambda  ; a.
                             let b = sub a 3.0
                             in (b,) }
                           { lambda  ; a.
                             let b = add a 3.0
                             in (b,) } )
                linear=(False,) ] c a
  in (d,) }

In this case, the boolean predicate is converted to an integer index (0 or 1), and branches are jaxprs that correspond to the false and true branch functionals, in that order. Again, each functional takes one input variable, corresponding to xtrue and xfalse respectively.

The following example shows a more complicated situation when the input to the branch functionals is a tuple, and the false branch functional contains a constant jnp.ones(1) that is hoisted as a constvar

>>> def func8(arg1, arg2):  # arg2 is a pair
...   return lax.cond(arg1 >= 0.,
...                   lambda xtrue: xtrue[0],
...                   lambda xfalse: jnp.array([1]) + xfalse[1],
...                   arg2)
>>> print(make_jaxpr(func8)(5., (jnp.zeros(1), 2.)))
{ lambda a ; b c d.
  let e = ge b 0.0
      f = convert_element_type[ new_dtype=int32
                                old_dtype=bool ] e
      g = cond[ branches=( { lambda  ; a b c.
                             let d = convert_element_type[ new_dtype=float32
                                                           old_dtype=int32 ] a
                                 e = add d c
                             in (e,) }
                           { lambda  ; f_ a b.
                             in (a,) } )
                linear=(False, False, False) ] f a c d
  in (g,) }


Just like for conditionals, Python loops are inlined during tracing. If you want to capture a loop for dynamic execution, you must use one of several special operations, jax.lax.while_loop() (a primitive) and jax.lax.fori_loop() (a helper that generates a while_loop primitive):

lax.while_loop(cond_fun: (C -> bool), body_fun: (C -> C), init: C) -> C
lax.fori_loop(start: int, end: int, body: (int -> C -> C), init: C) -> C

In the above signature, “C” stands for the type of a the loop “carry” value. For example, here is an example fori loop

>>> import numpy as np
>>> def func10(arg, n):
...   ones = jnp.ones(arg.shape)  # A constant
...   return lax.fori_loop(0, n,
...                        lambda i, carry: carry + ones * 3. + arg,
...                        arg + ones)
>>> print(make_jaxpr(func10)(np.ones(16), 5))
{ lambda  ; a b.
  let c = broadcast_in_dim[ broadcast_dimensions=(  )
                            shape=(16,) ] 1.0
      d = add a c
      _ _ e = while[ body_jaxpr={ lambda  ; a b c d e.
                                  let f = add c 1
                                      g = mul a 3.0
                                      h = add e g
                                      i = add h b
                                  in (f, d, i) }
                     cond_jaxpr={ lambda  ; a b c.
                                  let d = lt a b
                                  in (d,) }
                     cond_nconsts=0 ] c a 0 b d
  in (e,) }

The while primitive takes 5 arguments: c a 0 b e, as follows:

  • 0 constants for cond_jaxpr (since cond_nconsts is 0)

  • 2 constants for body_jaxpr (c, and a)

  • 3 parameters for the initial value of carry


JAX supports a special form of loop over the elements of an array (with statically known shape). The fact that there are a fixed number of iterations makes this form of looping easily reverse-differentiable. Such loops are constructed with the jax.lax.scan() function:

lax.scan(body_fun: (C -> A -> (C, B)), init_carry: C, in_arr: Array[A]) -> (C, Array[B])

Here C is the type of the scan carry, A is the element type of the input array(s), and B is the element type of the output array(s).

For the example consider the function func11 below

>>> def func11(arr, extra):
...   ones = jnp.ones(arr.shape)  #  A constant
...   def body(carry, aelems):
...     # carry: running dot-product of the two arrays
...     # aelems: a pair with corresponding elements from the two arrays
...     ae1, ae2 = aelems
...     return (carry + ae1 * ae2 + extra, carry)
...   return lax.scan(body, 0., (arr, ones))
>>> print(make_jaxpr(func11)(np.ones(16), 5.))
{ lambda  ; a b.
  let c = broadcast_in_dim[ broadcast_dimensions=(  )
                            shape=(16,) ] 1.0
      d e = scan[ jaxpr={ lambda  ; a b c d.
                          let e = mul c d
                              f = add b e
                              g = add f a
                          in (g, b) }
                  linear=(False, False, False, False)
                  unroll=1 ] b 0.0 a c
  in (d, e) }

The linear parameter describes for each of the input variables whether they are guaranteed to be used linearly in the body. Once the scan goes through linearization, more arguments will be linear.

The scan primitive takes 4 arguments: b 0.0 a c, of which:

  • one is the free variable for the body

  • one is the initial value of the carry

  • The next 2 are the arrays over which the scan operates.


The call primitive arises from JIT compilation, and it encapsulates a sub-jaxpr along with parameters the specify the backend and the device the computation should run. For example

>>> from jax import jit
>>> def func12(arg):
...   @jit
...   def inner(x):
...     return x + arg * jnp.ones(1)  # Include a constant in the inner function
...   return arg + inner(arg - 2.)
>>> print(make_jaxpr(func12)(1.))
{ lambda  ; a.
  let b = sub a 2.0
      c = xla_call[ backend=None
                    call_jaxpr={ lambda  ; a b.
                                 let c = broadcast_in_dim[ broadcast_dimensions=(  )
                                                           shape=(1,) ] 1.0
                                     d = mul a c
                                     e = add b d
                                 in (e,) }
                    donated_invars=(False, False)
                    name=inner ] a b
      d = add a c
  in (d,) }


If you use the jax.pmap() transformation, the function to be mapped is captured using the xla_pmap primitive. Consider this example

>>> from jax import pmap
>>> def func13(arr, extra):
...   def inner(x):
...     # use a free variable "extra" and a constant jnp.ones(1)
...     return (x + extra + jnp.ones(1)) / lax.psum(x, axis_name='rows')
...   return pmap(inner, axis_name='rows')(arr)
>>> print(make_jaxpr(func13)(jnp.ones((1, 3)), 5.))
{ lambda  ; a b.
  let c = xla_pmap[ axis_name=rows
                    call_jaxpr={ lambda  ; a b.
                                 let c = add b a
                                     d = broadcast_in_dim[ broadcast_dimensions=(  )
                                                           shape=(1,) ] 1.0
                                     e = add c d
                                     f = psum[ axis_index_groups=None
                                               axis_name=rows ] b
                                     g = div e f
                                 in (g,) }
                    donated_invars=(False, False)
                    in_axes=(None, 0)
                    out_axes=(0,) ] b a
  in (c,) }

The xla_pmap primitive specifies the name of the axis (parameter rows) and the body of the function to be mapped as the call_jaxpr parameter. value of this parameter is a Jaxpr with 3 input variables:

The parameter in_axes specifies which of the input variables should be mapped and which should be broadcast. In our example, the value of extra is broadcast, the other input values are mapped.