Compilation#

MLX has a compile() function transformation which compiles computation graphs. Function compilation results in smaller graphs by merging common work and fusing certain operations. In many cases this can lead to big improvements in run-time and memory use.

Getting started with compile() is simple, but there are some edge cases that are good to be aware of for more complex graphs and advanced usage.

Basics of Compile#

Let’s start with a simple example:

def fun(x, y):
    return mx.exp(-x) + y

x = mx.array(1.0)
y = mx.array(2.0)

# Regular call, no compilation
# Prints: array(2.36788, dtype=float32)
print(fun(x, y))

# Compile the function
compiled_fun = mx.compile(fun)

# Prints: array(2.36788, dtype=float32)
print(compiled_fun(x, y))

The output of both the regular function and the compiled function is the same up to numerical precision.

The first time you call a compiled function, MLX will build the compute graph, optimize it, and generate and compile code. This can be relatively slow. However, MLX will cache compiled functions, so calling a compiled function multiple times will not initiate a new compilation. This means you should typically compile functions that you plan to use more than once.

def fun(x, y):
    return mx.exp(-x) + y

x = mx.array(1.0)
y = mx.array(2.0)

compiled_fun = mx.compile(fun)

# Compiled here
compiled_fun(x, y)

# Not compiled again
compiled_fun(x, y)

# Not compiled again
mx.compile(fun)(x, y)

There are some important cases to be aware of that can cause a function to be recompiled:

Changing the shape or number of dimensions
Changing the type of any of the inputs
Changing the number of inputs to the function

In certain cases only some of the compilation stack will be rerun (for example when changing the shapes) and in other cases the full compilation stack will be rerun (for example when changing the types). In general you should avoid compiling functions too frequently.

Another idiom to watch out for is compiling functions which get created and destroyed frequently. This can happen, for example, when compiling an anonymous function in a loop:

a = mx.array(1.0)
# Don't do this, compiles lambda at each iteration
for _ in range(5):
    mx.compile(lambda x: mx.exp(mx.abs(x)))(a)

Example Speedup#

The mlx.nn.gelu() is a nonlinear activation function commonly used with Transformer-based models. The implementation involves several unary and binary element-wise operations:

def gelu(x):
    return x * (1 + mx.erf(x / math.sqrt(2))) / 2

If you use this function with small arrays, it will be overhead bound. If you use it with large arrays it will be memory bandwidth bound. However, all of the operations in the gelu are fusible into a single kernel with compile(). This can speedup both cases considerably.

Let’s compare the runtime of the regular function versus the compiled function. We’ll use the following timing helper which does a warm up and handles synchronization:

import time

def timeit(fun, x):
    # warm up
    for _ in range(10):
        mx.eval(fun(x))

    tic = time.perf_counter()
    for _ in range(100):
        mx.eval(fun(x))
    toc = time.perf_counter()
    tpi = 1e3 * (toc - tic) / 100
    print(f"Time per iteration {tpi:.3f} (ms)")

Now make an array, and benchmark both functions:

x = mx.random.uniform(shape=(32, 1000, 4096))
timeit(nn.gelu, x)
timeit(mx.compile(nn.gelu), x)

On an M1 Max the times are 15.5 and 3.1 milliseconds. The compiled gelu is five times faster.

Debugging#

When a compiled function is first called, it is traced with placeholder inputs. This means you can’t evaluate arrays (for example to print their contents) inside compiled functions.

@mx.compile
def fun(x):
    z = -x
    print(z)  # Crash
    return mx.exp(z)

fun(mx.array(5.0))

For debugging, inspecting arrays can be helpful. One way to do that is to globally disable compilation using the disable_compile() function or MLX_DISABLE_COMPILE flag. For example the following is okay even though fun is compiled:

@mx.compile
def fun(x):
    z = -x
    print(z) # Okay
    return mx.exp(z)

mx.disable_compile()
fun(mx.array(5.0))

Pure Functions#

Compiled functions are intended to be pure; that is they should not have side effects. For example:

state = []

@mx.compile
def fun(x, y):
    z = x + y
    state.append(z)
    return mx.exp(z)

fun(mx.array(1.0), mx.array(2.0))
# Crash!
print(state)

After the first call of fun, the state list will hold a placeholder array. The placeholder does not have any data; it is only used to build the computation graph. Printing such an array results in a crash.

You have two options to deal with this. The first option is to simply return state as an output:

state = []

@mx.compile
def fun(x, y):
   z = x + y
   state.append(z)
   return mx.exp(z), state

 _, state = fun(mx.array(1.0), mx.array(2.0))
 # Prints [array(3, dtype=float32)]
 print(state)

In some cases returning updated state can be pretty inconvenient. Hence, compile() has a parameter to capture implicit outputs:

from functools import partial

state = []

# Tell compile to capture state as an output
@partial(mx.compile, outputs=state)
def fun(x, y):
    z = x + y
    state.append(z)
    return mx.exp(z), state

fun(mx.array(1.0), mx.array(2.0))
# Prints [array(3, dtype=float32)]
print(state)

This is particularly useful for compiling a function which includes an update to a container of arrays, as is commonly done when training the parameters of a mlx.nn.Module.

Compiled functions will also treat any inputs not in the parameter list as constants. For example:

state = [mx.array(1.0)]

@mx.compile
def fun(x):
    return x + state[0]

# Prints array(2, dtype=float32)
print(fun(mx.array(1.0)))

# Update state
state[0] = mx.array(5.0)

# Still prints array(2, dtype=float32)
print(fun(mx.array(1.0)))

In order to have the change of state reflected in the outputs of fun you again have two options. The first option is to simply pass state as input to the function. In some cases this can be pretty inconvenient. Hence, compile() also has a parameter to capture implicit inputs:

from functools import partial
state = [mx.array(1.0)]

# Tell compile to capture state as an input
@partial(mx.compile, inputs=state)
def fun(x):
    return x + state[0]

# Prints array(2, dtype=float32)
print(fun(mx.array(1.0)))

# Update state
state[0] = mx.array(5.0)

# Prints array(6, dtype=float32)
print(fun(mx.array(1.0)))

Compiling Training Graphs#

This section will step through how to use compile() with a simple example of a common setup: training a model with mlx.nn.Module using an mlx.optimizers.Optimizer with state. We will show how to compile the full forward, backward, and update with compile().

To start, here is the simple example without any compilation:

import mlx.core as mx
import mlx.nn as nn
import mlx.optimizers as optim

# 4 examples with 10 features each
x = mx.random.uniform(shape=(4, 10))

# 0, 1 targets
y = mx.array([0, 1, 0, 1])

# Simple linear model
model = nn.Linear(10, 1)

# SGD with momentum
optimizer = optim.SGD(learning_rate=0.1, momentum=0.8)

def loss_fn(model, x, y):
    logits = model(x).squeeze()
    return nn.losses.binary_cross_entropy(logits, y)

loss_and_grad_fn = nn.value_and_grad(model, loss_fn)

# Perform 10 steps of gradient descent
for it in range(10):
    loss, grads = loss_and_grad_fn(model, x, y)
    optimizer.update(model, grads)
    mx.eval(model.parameters(), optimizer.state)

To compile the update we can put it all in a function and compile it with the appropriate input and output captures. Here’s the same example but compiled:

import mlx.core as mx
import mlx.nn as nn
import mlx.optimizers as optim
from functools import partial

# 4 examples with 10 features each
x = mx.random.uniform(shape=(4, 10))

# 0, 1 targets
y = mx.array([0, 1, 0, 1])

# Simple linear model
model = nn.Linear(10, 1)

# SGD with momentum
optimizer = optim.SGD(learning_rate=0.1, momentum=0.8)

def loss_fn(model, x, y):
    logits = model(x).squeeze()
    return nn.losses.binary_cross_entropy(logits, y)

# The state that will be captured as input and output
state = [model.state, optimizer.state]

@partial(mx.compile, inputs=state, outputs=state)
def step(x, y):
    loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
    loss, grads = loss_and_grad_fn(model, x, y)
    optimizer.update(model, grads)
    return loss

# Perform 10 steps of gradient descent
for it in range(10):
    loss = step(x, y)
    # Evaluate the model and optimizer state
    mx.eval(state)
    print(loss)

Note

If you are using a module which performs random sampling such as mlx.nn.Dropout(), make sure you also include mx.random.state in the state captured by compile(), i.e. state = [model.state, optimizer.state, mx.random.state].

Note

For more examples of compiling full training graphs checkout the MLX Examples GitHub repo.

Transformations with Compile#

In MLX function transformations are composable. You can apply any function transformation to the output of any other function transformation. For more on this, see the documentation on function transforms.

Compiling transformed functions works just as expected:

grad_fn = mx.grad(mx.exp)

compiled_grad_fn = mx.compile(grad_fn)

# Prints: array(2.71828, dtype=float32)
print(grad_fn(mx.array(1.0)))

# Also prints: array(2.71828, dtype=float32)
print(compiled_grad_fn(mx.array(1.0)))

Note

In order to compile as much as possible, a transformation of a compiled function will not by default be compiled. To compile the transformed function simply pass it through compile().

You can also compile functions which themselves call compiled functions. A good practice is to compile the outer most function to give compile() the most opportunity to optimize the computation graph:

@mx.compile
def inner(x):
    return mx.exp(-mx.abs(x))

def outer(x):
    inner(inner(x))

# Compiling the outer function is good to do as it will likely
# be faster even though the inner functions are compiled
fun = mx.compile(outer)

Compilation

Contents

Compilation#

Basics of Compile#

Example Speedup#

Debugging#

Pure Functions#

Compiling Training Graphs#

Transformations with Compile#