Python Specification for DLPack

The Python specification for DLPack is a part of the Python array API standard. More details about the spec can be found under the Data interchange mechanisms page.

Syntax for data interchange with DLPack

The array API will offer the following syntax for data interchange:

  1. A from_dlpack(x) function, which accepts (array) objects with a __dlpack__ method and uses that method to construct a new array containing the data from x.

  2. __dlpack__(self, stream=None) and __dlpack_device__ methods on the array object, which will be called from within from_dlpack, to query what device the array is on (may be needed to pass in the correct stream, e.g. in the case of multiple GPUs) and to access the data.

Semantics

DLPack describes the memory layout of strided, n-dimensional arrays. When a user calls y = from_dlpack(x), the library implementing x (the “producer”) will provide access to the data from x to the library containing from_dlpack (the “consumer”). If possible, this must be zero-copy (i.e. y will be a view on x). If not possible, that library may make a copy of the data. In both cases:

  • The producer keeps owning the memory

  • y may or may not be a view, therefore the user must keep the recommendation to avoid mutating y in mind - see Copy-view behaviour and mutability.

  • Both x and y may continue to be used just like arrays created in other ways.

If an array that is accessed via the interchange protocol lives on a device that the requesting library does not support, it is recommended to raise a TypeError.

Stream handling through the stream keyword applies to CUDA and ROCm (perhaps to other devices that have a stream concept as well, however those haven’t been considered in detail). The consumer must pass the stream it will use to the producer; the producer must synchronize or wait on the stream when necessary. In the common case of the default stream being used, synchronization will be unnecessary so asynchronous execution is enabled.

Implementation

Note that while this API standard largely tries to avoid discussing implementation details, some discussion and requirements are needed here because data interchange requires coordination between implementers on, e.g., memory management.

Diagram of DLPack structs

DLPack diagram. Dark blue are the structs it defines, light blue struct members, gray text enum values of supported devices and data types.

The __dlpack__ method will produce a PyCapsule containing a DLManagedTensor, which will be consumed immediately within from_dlpack - therefore it is consumed exactly once, and it will not be visible to users of the Python API.

The producer must set the PyCapsule name to "dltensor" so that it can be inspected by name, and set PyCapsule_Destructor that calls the deleter of the DLManagedTensor when the "dltensor"-named capsule is no longer needed.

The consumer must transer ownership of the DLManangedTensor from the capsule to its own object. It does so by renaming the capsule to "used_dltensor" to ensure that PyCapsule_Destructor will not get called (ensured if PyCapsule_Destructor calls deleter only for capsules whose name is "dltensor"), but the deleter of the DLManagedTensor will be called by the destructor of the consumer library object created to own the DLManagerTensor obtained from the capsule. Below is an example of the capsule deleter written in the Python C API which is called either when the refcount on the capsule named "dltensor" reaches zero or the consumer decides to deallocate its array:

static void dlpack_capsule_deleter(PyObject *self){
   if (PyCapsule_IsValid(self, "used_dltensor")) {
      return; /* Do nothing if the capsule has been consumed. */
   }

   /* an exception may be in-flight, we must save it in case we create another one */
   PyObject *type, *value, *traceback;
   PyErr_Fetch(&type, &value, &traceback);

   DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, "dltensor");
   if (managed == NULL) {
      PyErr_WriteUnraisable(self);
      goto done;
   }
   /* the spec says the deleter can be NULL if there is no way for the caller to provide a reasonable destructor. */
   if (managed->deleter) {
      managed->deleter(managed);
      /* TODO: is the deleter allowed to set a python exception? */
      assert(!PyErr_Occurred());
   }

done:
   PyErr_Restore(type, value, traceback);
}

Note: the capsule names "dltensor" and "used_dltensor" must be statically allocated.

When the strides field in the DLTensor struct is NULL, it indicates a row-major compact array. If the array is of size zero, the data pointer in DLTensor should be set to either NULL or 0.

DLPack version used must be 0.2 <= DLPACK_VERSION < 1.0. For further details on DLPack design and how to implement support for it, refer to github.com/dmlc/dlpack.

Warning

DLPack contains a device_id, which will be the device ID (an integer, 0, 1, ...) which the producer library uses. In practice this will likely be the same numbering as that of the consumer, however that is not guaranteed. Depending on the hardware type, it may be possible for the consumer library implementation to look up the actual device from the pointer to the data - this is possible for example for CUDA device pointers.

It is recommended that implementers of this array API consider and document whether the .device attribute of the array returned from from_dlpack is guaranteed to be in a certain order or not.

Reference Implementations

Several Python libraries have adopted this standard using Python C API, C++, Cython, ctypes, cffi, etc: