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Integrate TVM into Your Project
===============================

TVM's runtime is designed to be lightweight and portable.
There are several ways you can integrate TVM into your project.

This article introduces possible ways to integrate TVM
as a JIT compiler to generate functions on your system.


DLPack Support
--------------

TVM's generated function follows the PackedFunc convention.
It is a function that can take positional arguments including
standard types such as float, integer, string.
The PackedFunc takes DLTensor pointer in `DLPack <https://github.com/dmlc/dlpack>`_ convention.
So the only thing you need to solve is to create a corresponding DLTensor object.



Integrate User Defined C++ Array
--------------------------------

The only thing we have to do in C++ is to convert your array to DLTensor and pass in its address as
``DLTensor*`` to the generated function.


Integrate User Defined Python Array
-----------------------------------

Assume you have a python object ``MyArray``. There are three things that you need to do

- Add ``_tvm_tcode`` field to your array which returns ``tvm.TypeCode.ARRAY_HANDLE``
- Support ``_tvm_handle`` property in your object, which returns the address of DLTensor in python integer
- Register this class by ``tvm.register_extension``

.. code:: python

   # Example code
   import tvm

   class MyArray(object):
       _tvm_tcode = tvm.TypeCode.ARRAY_HANDLE

       @property
       def _tvm_handle(self):
           dltensor_addr = self.get_dltensor_addr()
           return dltensor_addr

   # You can put registration step in a separate file mypkg.tvm.py
   # and only optionally import that if you only want optional dependency.
   tvm.register_extension(MyArray)