# Understanding JaxprsÂ¶

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+] }
```

- where:
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+
```

- where:
`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 PyTreesÂ¶

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 VarsÂ¶

Some values in jaxprs are constants, in that their value does not depend on the jaxprâ€™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 (â€śconstvarsâ€ť). These constvars differ from the other jaxpr parameters (â€śinvarsâ€ť) only as a bookkeeping convention.

## Higher-order primitivesÂ¶

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

### ConditionalsÂ¶

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.
let
in (a,) } )
linear=(False, False, False) ] f a c d
in (g,) }
```

### WhileÂ¶

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, â€śCâ€ť stands for the type of a the loop â€ścarryâ€ť 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) }
body_nconsts=2
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

### ScanÂ¶

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) }
length=16
linear=(False, False, False, False)
num_carry=1
num_consts=1
reverse=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.

### XLA_callÂ¶

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,) }
device=None
donated_invars=(False, False)
name=inner ] a b
d = add a c
in (d,) }
```

### XLA_pmapÂ¶

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
axis_size=1
backend=None
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,) }
devices=None
donated_invars=(False, False)
global_axis_size=None
mapped_invars=(False, True)
name=inner ] 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 `mapped_invars`

specify 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.