Hi, I've just came across a model that requires support for ONNX CumSum op https://github.com/onnx/onnx/blob/master/docs/Operators.md#CumSum. The model comes from DETR object detection model https://github.com/facebookresearch/detr. Since this model doesn't need ad hoc object detection ops that are painful to support, I think it is a great fit for TVM. Our ONNX frontend (also PyTorch) only needs to implement Cumsum op.
Since TVM has support for scan operation https://tvm.apache.org/docs/tutorials/language/scan.html#sphx-glr-tutorials-language-scan-py, I'm wondering if it is a good idea to implement Relay cumsum op on top of te scan, or implement a new topi operator from scratch. I also want to utilize scan primitive from thrust to support fast cumsum on cuda. @tqchen @kevinthesun @Laurawly @jwfromm --- [Visit Topic](https://discuss.tvm.apache.org/t/supporting-cumsum-from-onnx-use-te-scan-op-or-develop-from-scratch/7830/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/b1f0ea64da5c455627578bc564be6dcea38fc140d3c5c8902e08e38aa6daa68c).
