docs/src/usage/numpy.rst
.. _numpy:
MLX array supports conversion between other frameworks with either:
Python Buffer Protocol <https://docs.python.org/3/c-api/buffer.html>_.DLPack <https://dmlc.github.io/dlpack/latest/>_.Let's convert an array to NumPy and back.
.. code-block:: python
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:
.. code-block:: python
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:
.. code-block:: python
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 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:
.. code-block:: python
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:
.. code-block:: python
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.
.. code-block:: python
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
.. code-block:: python
a = mx.arange(3, dtype=mx.float32) mx.eval(a) b = torch.as_tensor(a) # zero-copy DLPack import on Metal
JAX fully supports the buffer protocol.
.. code-block:: python
import mlx.core as mx import jax.numpy as jnp
a = mx.arange(3) b = jnp.array(a) c = mx.array(b)
TensorFlow supports the buffer protocol, but it requires an explicit
:obj:memoryview.
.. code-block:: python
import mlx.core as mx import tensorflow as tf
a = mx.arange(3) b = tf.constant(memoryview(a)) c = mx.array(b)