Conversion to NumPy and Other Frameworks

MLX array implements the Python Buffer Protocol. Let’s convert an array to NumPy and back.

import mlx.core as mx
import numpy as np

a = mx.arange(3)
b = np.array(a) # copy of a
c = mx.array(b) # copy of b

Note

Since NumPy does not support bfloat16 arrays, you will need to convert to float16 or float32 first: np.array(a.astype(mx.float32)). Otherwise, you will receive an error like: Item size 2 for PEP 3118 buffer format string does not match the dtype V item size 0.

By default, NumPy copies data to a new array. This can be prevented by creating an array view:

a = mx.arange(3)
a_view = np.array(a, copy=False)
print(a_view.flags.owndata) # False
a_view[0] = 1
print(a[0].item()) # 1

A NumPy array view is a normal NumPy array, except that it does not own its memory. This means writing to the view is reflected in the original array.

While this is quite powerful to prevent copying arrays, it should be noted that external changes to the memory of arrays cannot be reflected in gradients.

Let’s demonstrate this in an example:

def f(x):
    x_view = np.array(x, copy=False)
    x_view[:] *= x_view # modify memory without telling mx
    return x.sum()

x = mx.array([3.0])
y, df = mx.value_and_grad(f)(x)
print("f(x) = x² =", y.item()) # 9.0
print("f'(x) = 2x !=", df.item()) # 1.0

The function f indirectly modifies the array x through a memory view. However, this modification is not reflected in the gradient, as seen in the last line outputting 1.0, representing the gradient of the sum operation alone. The squaring of x occurs externally to MLX, meaning that no gradient is incorporated. It’s important to note that a similar issue arises during array conversion and copying. For instance, a function defined as mx.array(np.array(x)**2).sum() would also result in an incorrect gradient, even though no in-place operations on MLX memory are executed.

PyTorch

Warning

PyTorch Support for memoryview is experimental and can break for multi-dimensional arrays. Casting to NumPy first is advised for now.

PyTorch supports the buffer protocol, but it requires an explicit memoryview.

import mlx.core as mx
import torch

a = mx.arange(3)
b = torch.tensor(memoryview(a))
c = mx.array(b.numpy())

Conversion from PyTorch tensors back to arrays must be done via intermediate NumPy arrays with numpy().

JAX

JAX fully supports the buffer protocol.

import mlx.core as mx
import jax.numpy as jnp

a = mx.arange(3)
b = jnp.array(a)
c = mx.array(b)

TensorFlow

TensorFlow supports the buffer protocol, but it requires an explicit memoryview.

import mlx.core as mx
import tensorflow as tf

a = mx.arange(3)
b = tf.constant(memoryview(a))
c = mx.array(b)