# Extension module with metadata **URL:** \ **Category:** Uncategorized\ **Created:** [May 8, 2023, 3:04pm UTC](https://dev-discuss.pytorch.org/t/extension-module-with-metadata/1253 "2023-05-08T15:04:35Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![FranciscoVasconcelos](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/franciscovasconcelos/32/1138_2.png) [@FranciscoVasconcelos](https://dev-discuss.pytorch.org/u/FranciscoVasconcelos)\ **Post date:** [May 8, 2023, 3:04pm UTC](https://dev-discuss.pytorch.org/t/extension-module-with-metadata/1253/1 "2023-05-08T15:04:35Z")
Hello, I’m working on a an extension module that adds operations for geometric algebra. This operations usually require some metadata that is essential for them. I have built a python extension module where all of these operations reside but they are defined for a default data type. I wanted to be able to initialize my module with some parameters which define my algebra and which selects a set of functions to use and also generates the metadata I need. The extension module I’ve built already does that and it can work with different algebras. And a bunch of different data types. The data types I have are sparse multivectors which are similar to sparse matrices where they have a value and a corresponding bitmap (it encodes the basis blades of the algebra) that goes from 0 to the size of the algebra. I’m having difficulty on how I would integrate everything in a pytorch c++ extension module. Your help would be much appreciated. ---
**Author:** ![albanD](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/alband/32/9_2.png) [@albanD](https://dev-discuss.pytorch.org/u/albanD)\ **Post date:** [May 9, 2023, 1:31pm UTC](https://dev-discuss.pytorch.org/t/extension-module-with-metadata/1253/2 "2023-05-09T13:31:23Z")
Hey! It is a bit hard to say without more details. Does the doc at [Custom C++ and CUDA Extensions — PyTorch Tutorials 2.0.0+cu117 documentation](https://pytorch.org/tutorials/advanced/cpp_extension.html) help? ---
**Author:** ![FranciscoVasconcelos](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/franciscovasconcelos/32/1138_2.png) [@FranciscoVasconcelos](https://dev-discuss.pytorch.org/u/FranciscoVasconcelos)\ **Post date:** [May 18, 2023, 11:35am UTC](https://dev-discuss.pytorch.org/t/extension-module-with-metadata/1253/3 "2023-05-18T11:35:26Z")
Hello, I’ve followed some of those tutorials, that one included, and I was able to write a CUDA and a C++ kernel for the operations I needed. I guess I’ll have to write things from scratch to be able to work with the C++ API or I could try to convert tensors to the types I’m using. Also will try to use sparse tensors to do computations. There are some things that I can directly port as the algebra generation module. But I’ve still have to figure out how to from a chosen algebra chose the best kernels. I want to abstract the user on the implementation of the kernels. My ideia is to have a bunch of kernels, CUDA and C++, which are chosen depending on the algebra that is chosen. I want to have a generic kernel that works with sparse and dense multivectors. I want to have code generated kernels that are computationally less expensive for smaller algebras. Essentialy writing a dynamic module that does all the kernel choices for you. Thanks in advance. ---
**Author:** ![FranciscoVasconcelos](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/franciscovasconcelos/32/1138_2.png) [@FranciscoVasconcelos](https://dev-discuss.pytorch.org/u/FranciscoVasconcelos)\ **Post date:** [May 19, 2023, 6:45pm UTC](https://dev-discuss.pytorch.org/t/extension-module-with-metadata/1253/4 "2023-05-19T18:45:47Z")
Maybe I have to ask the question in a different way. I have a bunch of kernels. I’m going to put this kernels in tables of kernels. So I will have a table for each type implementation. I want to be able to select these tables based on the input of the user. For example if I start my python module with `ga = GA(dtype='sparse')` I want ga to have all the kernels available for that type implementation. Which means that I could call PyTorch modules from `ga`, that is `ga.ReLu(x)` or `ga.multivector([1,2,3,4])`. And operations that I do with objects which I create with this module should call the selected kernel, that is ```python x = ga.multivector([1,2,3,4]) y = ga.multivector([5,6,7,8]) y*x # This should call the selected kernel ``` ---
**Author:** ![albanD](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/alband/32/9_2.png) [@albanD](https://dev-discuss.pytorch.org/u/albanD)\ **Post date:** [May 22, 2023, 12:14pm UTC](https://dev-discuss.pytorch.org/t/extension-module-with-metadata/1253/5 "2023-05-22T12:14:41Z")
I’m not sure what the question is? But if you want Tensor objects that have a different behavior from the vanilla ones. I would suggest to define a subclass of Tensor that does what you want. Some partial doc on this is at [Extending PyTorch — PyTorch 2.0 documentation](https://pytorch.org/docs/stable/notes/extending.html#extending-torch)