Conversion to NumPy and Other Frameworks#

MLX array supports conversion between other frameworks with either:

The Python Buffer Protocol.
DLPack.

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

Note

NumPy arrays with type float64 will be default converted to MLX arrays with type float32.

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#

PyTorch supports DLPack inputs and can import MLX arrays directly. MLX can also import PyTorch tensors through DLPack with mx.asarray or mx.from_dlpack. Use torch.as_tensor to import an MLX array with DLPack; torch.tensor copies the data instead. Similarly, mx.asarray can share DLPack inputs when possible, while mx.array copies:

import mlx.core as mx
import torch

a = mx.arange(3, dtype=mx.float32)
mx.eval(a)

shared = torch.as_tensor(a)
copied = torch.tensor(a)

Creating an MLX array from a CPU tensor copies the data into MLX-owned storage. The arrays do not share memory:

b = torch.arange(3)
c = mx.array(b)

b += 10
print(c.tolist()) # [0, 1, 2]

Metal DLPack inputs are different. If a PyTorch MPS tensor is passed to mx.asarray or to mx.from_dlpack with copy=None, MLX imports it without a copy when the underlying Metal buffer is not private. Private Metal buffers are copied into MLX-managed storage instead. Passing copy=False requires zero-copy import and raises an error if a copy would be needed. Passing copy=True asks MLX to create a new array instead of reusing the Metal buffer. Zero-copy imports preserve the DLPack strides. mx.array also creates a new array instead of reusing the Metal buffer. MLX arrays exported to PyTorch with DLPack are exported without a copy on Metal.

In particular, PyTorch 2.12 and later use shared storage for ordinary MPS tensors on Apple silicon, while older PyTorch versions may use private storage and require a copy on import. DLPack conversion does not synchronize pending Metal work; synchronize or evaluate the producing framework before reading the converted array.

b = torch.arange(3, device="mps", dtype=torch.float32)
torch.mps.synchronize()
c = mx.asarray(b) # zero-copy if the Metal buffer can be reused
d = mx.from_dlpack(b, copy=True) # explicit copy

a = mx.arange(3, dtype=mx.float32)
mx.eval(a)
b = torch.as_tensor(a) # zero-copy DLPack import on Metal

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)

Conversion to NumPy and Other Frameworks

Contents

Conversion to NumPy and Other Frameworks#

PyTorch#

JAX#

TensorFlow#