Update

Author: Micky774

jax/_src/array.py

@@ -404,11 +404,25 @@ def __array__(self, dtype=None, context=None, copy=None):
     kwds = {} if copy is None else {'copy': copy}
     return np.asarray(self._value, dtype=dtype, **kwds)
 
-  def __dlpack__(self, *, stream: int | Any | None = None):
-    if len(self._arrays) != 1:
-      raise BufferError("__dlpack__ only supported for unsharded arrays.")
+  def __dlpack__(self, *, stream: int | Any | None = None,
+                 max_version: tuple[int, int] | None = None,
+                 dl_device: tuple[enum.Enum, int] | None = None,
+                 copy: bool | None = None):
     from jax._src.dlpack import to_dlpack  # pylint: disable=g-import-not-at-top
-    return to_dlpack(self, stream=stream)
+
+    device_set = self.sharding.device_set
+    if len(device_set) > 1:
+      raise BufferError(
+        "to_dlpack can only pack a dlpack tensor from an array on a singular "
+        f"device, but an array with a Sharding over {len(device_set)} devices "
+        "was provided."
+      )
+    device, = device_set
+    return to_dlpack(self, stream=stream,
+                     max_version=max_version,
+                     device=device,
+                     dl_device=dl_device, # type: ignore
+                     copy=copy)
 
   def __dlpack_device__(self) -> tuple[enum.Enum, int]:
     if len(self._arrays) != 1:

jax/_src/dlpack.py

@@ -24,7 +24,10 @@
 from jax._src.lib import xla_client
 from jax._src.lib import xla_extension_version
 from jax._src.typing import Array
+from jax._src.api import device_put
 
+DLPACK_VERSION = (0, 1)
+MIN_DLPACK_VERSION = (0, 1)
 
 # A set of dtypes that dlpack supports.
 # Note: Make sure to use a "type", not a dtype instance, when looking up this set
@@ -48,9 +51,32 @@ class DLDeviceType(enum.IntEnum):
   kDLCUDA = 2
   kDLROCM = 10
 
+def _to_dlpack(x: Array, stream: int | Any | None,
+               device: xla_client.Device | None = None,
+               dlpack_device: xla_client.Device | None = None,
+               copy: bool | None = None):
+  if dlpack_device and dlpack_device != device:
+    if copy is not None and not copy:
+      raise ValueError(
+        f"Specified {dlpack_device=} which requires a copy since the source device "
+        f"is {repr(device)}, however copy=False. Set copy=True or "
+        "copy=None to perform the requested operation."
+      )
+    else:
+      arr = device_put(x, dlpack_device)
+  else:
+    arr = x.copy() if copy else x
+
+  return xla_client._xla.buffer_to_dlpack_managed_tensor(
+    arr.addressable_data(0), stream=stream
+  )
 
 def to_dlpack(x: Array, take_ownership: bool = False,
-              stream: int | Any | None = None):
+              stream: int | Any | None = None,
+              device: xla_client.Device | None = None,
+              dl_device: tuple[DLDeviceType, int] | None = None,
+              max_version: tuple[int, int] | None = None,
+              copy : bool | None = None):
   """Returns a DLPack tensor that encapsulates a :class:`~jax.Array` ``x``.
 
   Args:
@@ -73,14 +99,40 @@ def to_dlpack(x: Array, take_ownership: bool = False,
   if not isinstance(x, array.ArrayImpl):
     raise TypeError("Argument to to_dlpack must be a jax.Array, "
                     f"got {type(x)}")
-  assert len(x.devices()) == 1
   if take_ownership:
     warnings.warn(
         "take_ownership in to_dlpack is deprecated and it is a no-op."
     )
-  return xla_client._xla.buffer_to_dlpack_managed_tensor(
-      x.addressable_data(0), stream=stream
-  )  # type: ignore
+
+  dlpack_device = None
+  dl_device_type, local_hardware_id = dl_device if dl_device else (None, None)
+  if dl_device_type:
+    try:
+      dl_device_platform = {
+          DLDeviceType.kDLCPU: "cpu",
+          DLDeviceType.kDLCUDA: "cuda",
+          DLDeviceType.kDLROCM: "rocm",
+      }[dl_device_type]
+      backend = xla_bridge.get_backend(dl_device_platform)
+      dlpack_device = backend.device_from_local_hardware_id(local_hardware_id)
+    except TypeError:
+      # https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__dlpack__.html
+      # recommends using BufferError.
+      raise BufferError(
+          "The device specification passed to to_dlpack contains an unsupported "
+          f"device type (DLDeviceType: {dl_device_type})")
+
+  if max_version is None or max_version[0] >= DLPACK_VERSION[0]:
+    return _to_dlpack(x, stream=stream, device=device, dlpack_device=dlpack_device, copy=copy)
+  elif max_version >= MIN_DLPACK_VERSION:
+    # Legacy path to be implemented when XLA adopts DLManagedTensorVersioned format
+    raise RuntimeError("This branch should be unreachable. "
+                       "Please open a bug if you see this.")
+  else:
+    raise BufferError(
+      f"JAX does not support any version below {MIN_DLPACK_VERSION} but "
+      f"version ({max_version}) was requested."
+    )
 
 
 def from_dlpack(external_array):
@@ -110,12 +162,12 @@ def from_dlpack(external_array):
           DLDeviceType.kDLCUDA: "cuda",
           DLDeviceType.kDLROCM: "rocm",
       }[dl_device_type]
-    except TypeError:
+    except TypeError as err:
       # https://dmlc.github.io/dlpack/latest/python_spec.html recommends using
       # TypeError.
-      raise TypeError(
+      raise BufferError(
           "Array passed to from_dlpack is on unsupported device type "
-          f"(DLDeviceType: {dl_device_type}, array: {external_array}")
+          f"(DLDeviceType: {dl_device_type}, array: {external_array}") from err
 
     backend = xla_bridge.get_backend(device_platform)
     device = backend.device_from_local_hardware_id(device_id)