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. If you are looking for minimum deployment of a compiled module, take a look at deployment guide

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 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
# Example code
import tvm

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

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

# You can put registration step in a separate file
# and only optionally import that if you only want optional dependency.