Construct a JAX array via DLPack.
JAX implementation of numpy.from_dlpack().
x (Any) – An object that implements the DLPack protocol via the __dlpack__ and __dlpack_device__ methods, or a legacy DLPack tensor on either CPU or GPU.
device (xc.Device | Sharding | None | None) – An optional Device or Sharding, representing the single device onto which the returned array should be placed. If given, then the result is committed to the device. If unspecified, the resulting array will be unpacked onto the same device it originated from. Setting device to a device different from the source of external_array will require a copy, meaning copy must be set to either True or None.
copy (bool | None | None) – An optional boolean, controlling whether or not a copy is performed. If copy=True then a copy is always performed, even if unpacked onto the same device. If copy=False then the copy is never performed and will raise an error if necessary. When copy=None (default) then a copy may be performed if needed for a device transfer.
A JAX array of the imput buffer.
Note
While JAX arrays are always immutable, dlpack buffers cannot be marked as immutable, and it is possible for processes external to JAX to mutate them in-place. If a JAX Array is constructed from a dlpack buffer without copying and the source buffer is later modified in-place, it may lead to undefined behavior when using the associated JAX array.
Examples
Passing data between NumPy and JAX via DLPack:
>>> import numpy as np >>> rng = np.random.default_rng(42) >>> x_numpy = rng.random(4, dtype='float32') >>> print(x_numpy) [0.08925092 0.773956 0.6545715 0.43887842] >>> hasattr(x_numpy, "__dlpack__") # NumPy supports the DLPack interface True
>>> import jax.numpy as jnp >>> x_jax = jnp.from_dlpack(x_numpy) >>> print(x_jax) [0.08925092 0.773956 0.6545715 0.43887842] >>> hasattr(x_jax, "__dlpack__") # JAX supports the DLPack interface True
>>> x_numpy_round_trip = np.from_dlpack(x_jax) >>> print(x_numpy_round_trip) [0.08925092 0.773956 0.6545715 0.43887842]
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